ISSN 2188-5001
MATERIALS DESIGN AND
CHARACTERIZATION LABORATORYSUPERCOMPUTER CENTERACTIVITY REPORT

2022

THE UNIVERSITY OF TOKYO

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Materials Design and Characterization Laboratory (MDCL)
The MDCL was established as the third research facility of the Institute for Solid
State Physics (ISSP) when the latter was reorganized in May 1996. Its aim is to promotematerial science with an emphasis on the “DSC cycle”, where DSC stands for design,synthesis and characterization, three processes for developing new materials.
The MDCL consists of two sections, Materials Design (MD) section and Materials
Synthesis and Characterization (MSC) section. The Supercomputer Center of the ISSP
(SCC-ISSP) is placed in the MD section, while in the MSC section there are seven laboratories for joint use; Materials Synthesis Laboratory, Chemical Analysis Laboratory, X-ray
Diﬀraction Laboratory, Electron Microscope Laboratory, Electromagnetic Measurement
Laboratory, Spectroscopy Laboratory, and High-Pressure Synthesis Laboratory.
Almost all the facilities of the MDCL are open to scientists in Japan through theUser Programs conducted by two steering committees of the MDCL. One is the steeringcommittee of the SCC-ISSP, under which the Supercomputer Project Advisory Committee
is placed for reviewing proposals. The other is the steering committee of the MSC facilities.
More than half of the members of these committees are from the outside of ISSP.

COVER FIGURE
Conformation of an ultra-long chain fatty acid, dTSPC, in DSPC bilayer. The dTSPC
molecule is represented by colored spheres.
See Page 35–43, K. Kawaguchi and H. Noguchi, ”Molecular dynamics study of ultra-longchain fatty acid in lipid bilayer”.

iiActivity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

PREFACEThe Supercomputer Center (SCC) is a part of the Materials Design and Characterization
Laboratory (MDCL) of ISSP. Its mission is to serve the whole community of computationalcondensed-matter physics of Japan providing it with high performance computing environment.
In particular, the SCC selectively promotes and supports large-scale computations. For thispurpose, the SCC invites proposals for supercomputer-aided research projects and hosts theSteering Committee, as mentioned below, that evaluates the proposals.
The ISSP supercomputer system consists of two subsystems: System B, which was last replacedin Oct. 2020, is intended for larger total computational power and has more nodes withrelatively loose connections whereas System C is intended for higher communication speed
among nodes. System B (ohtaka) consists of 1680 CPU nodes of AMD EPYC 7702 (64 cores)
and 8 FAT nodes of Intel Xeon Platinum 8280 (28 cores) with total theoretical performance of
6.881 PFlops. System C was replaced in June 2022 and the current system (kugui) consists of
128 CPU nodes of AMD EPYC 7763 (64 cores) and 8 ACC nodes of AMD EPYC 7763 (64cores) NVIDIA A100 40GB for HGX with total theoretical performance of 0.973 PFLOPS.
In addition to the hardware administration, the SCC puts increasing effort on the software
support. Since 2015, the SCC has been conducting “Project for advancement of software
usability in materials science (PASUMS).”In this project, for enhancing the usability of the
ISSP supercomputer system, we conduct several software-advancement activities: developing
new application software that runs efficiently on the ISSP supercomputer system, adding new
functions to existing codes, help releasing private codes for public use, creating/improving
manuals for public codes, etc. Two target programs were selected for fiscal year 2022: (1)abICS (proposal made by S. Kasamatsu (Yamagata U.)), and (2) H-wave (proposal made by A.
Kobayashi (Nagoya U.)). In addition, since 2021, we have been maintaining the data repository
service for secure storage and enhanced usability of results of numerical calculation.
All staff members of university faculties or public research institutes in Japan are invited topropose research projects (called User Program). The proposals are evaluated by the Steering
Committee of SCC. Peer-reviewing is done by the Supercomputer Project Advisory Committee.
In fiscal year 2022, totally 337 projects were approved including the ones under the frameworkof Supercomputing Consortium for Computational Materials Science (SCCMS), which
specially supports FUGAKU and other major projects in computational materials science.
The research projects are roughly classified into the following three (the number of projects
approved):
First-Principles Calculation of Materials Properties (151)
Strongly Correlated Quantum Systems (25)
Cooperative Phenomena in Complex, Macroscopic Systems (141)In all the three categories, most proposals involve both methodology and applications. The
results of the projects are reported in 'Activity Report 2022' of the SCC. Every year 3-4 projects
are selected for “invited papers” and published at the beginning of the Activity Report. In the
Activity Report 2022, the following three invited papers are included:``Training method for refinement of machine-learning interatomic potential and its
applications'',
Kohei SHIMAMURA, Akihide KOURA, and Fuyuki SHIMOJO (Kumamoto Univ.)iii

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo``First principles calculation of copper-oxide superconductors exhibiting electron
fractionalization'',Masatoshi IMADA (Waseda Univ.)``Molecular dynamics study of ultra-long chain fatty acid in lipid bilayer'',
Kazutomo KAWAGUCHI (Kanazawa Univ.) and Hiroshi NOGUCHI (ISSP)
June 2, 2023
Naoki Kawashima(Chairman of the steering committee, SCC, ISSP)

iv

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

CONTENTSPREFACE
1 OUTLINE1

1.1 Supercomputer System
1.2 Project Proposals
1.3 Committees
1.4 Staﬀ

1
1
3
7

2 STATISTICS (SCHOOL YEAR 2022)
2.1 System and User Statistics
2.2 Queue and Job Statistics
2.3 Project for Advancement of Software Usability
in Materials Science
2.4 ISSP Data Repository
3 RESEARCH REPORTS

7
7
8
10
11
14

3.1 Invited Articles
3.2 First-Principles Calculation of Material Properties
3.3 Strongly Correlated Quantum Systems
3.4 Cooperative Phenomena in Complex Macroscopic
Systems
3.5 SCCMS Projects
3.6 Software Advancement Projects
and Workshop Support
4 PUBLICATION LIST

14
44
188
213
352
373

381

ISSP Joint Research Projects
SCCMS Projects
Doctor Theses
Master Theses

382
434
439
441

v

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

1

OUTLINE

1.1

Supercomputer System

In School year 2022 (SY 2022), the ISSP supercomputer center provided users
with System B - Dell PowerEdge C6525/R940 system named “ohtaka” and System C - HPE Apollo 2000 Gen10 Plus/HPE Apollo 6500 Gen10 Plus system named“kugui”. System B is a massively-parallel supercomputer with two types of compute nodes: 8 “Fat” nodes and 1680 “CPU” nodes. “Fat” nodes are each comprisedof four Intel Xeon Platinum 8280 CPUs (28 cores/CPU) and 3 TB of memory per
node. “CPU” nodes have two AMD EPYC 7702 CPUs (64 cores/CPU) and 256
GB of memory per node. System B achieves about 6.881 PFLOPS in theoreticalpeak performance with high power eﬃciency. The subsystem comprised of only
CPU nodes ranks 87st in the Nov. 2020 Top 500 List, which is a ranking based
on total performance measured by the HPL benchmark. The compute nodes communicate to each other through HDR100 Inﬁniband and are connected in fat tree
topology. SY 2022 was the third year of the operation of the current System B. In
June 2022, “kugui” was introduced as System C, which will be operated for the
next six years. System C is a massively-parallel supercomputer with two typesof compute nodes: 128 “CPU” nodes and 8 “ACC” nodes. “CPU” nodes areeach comprised of two AMD EPYC 7763 CPUs (64 cores/CPU) and 256 GB ofmemory. The compute nodes communicate to each other through HDR200 Inﬁniband and are connected in fat tree topology. “ACC” nodes are each comprised of
one AMD EPYC 7763 CPUs (64 cores/CPU), four NVIDIA A100 40GB for HGXGPUs and 256 GB of memory. System C achieves 973 TFLOPS in theoreticalpeak performance. System C entered oﬃcial operation in June 14th, 2022. Trial
operation of System C continued until July 18th, 2022. SY 2022 was the ﬁrst yearof the operation of the current System C. For further details, please contact ISSP
Supercomputer Center (SCC-ISSP).
[Correspondence: center@issp.u-tokyo.ac.jp]

1.2

Project Proposals

The ISSP supercomputer system provides computation resources for scientists
working on condensed matter sciences in Japan. All scientiﬁc staﬀ members (including post-docs) at universities or public research institutes in Japan can submit
proposals for projects related to research activities on materials and condensed
matter sciences. These proposals are peer-reviewed by the Advisory Committee
members (see Sec. 1.3), and then the computation resources are allocated basedon the review reports. The leader of an approved project can set up user accounts
for collaborators. Other types of scientists, including graduate students, may also
be added. Proposal submissions, peer-review processes, and user registration are
all managed via a web system.
The computation resources are distributed in a unit called “point”, determined
as a function of available CPU utilization time and consumed disk resources. There1

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Figure 1: Supercomputer System at the SCC-ISSP

were calls for six classes of research projects in SY 2022. The number of projects
and the total number of points that were applied for and approved in this school
year are listed in Table 1.
• Class A is for trial use by new users; proposals for Class A projects are
accepted throughout the year.
• Proposals for projects in Classes B (small), C (mid-size), E (large-scale), andS (exceptional) can be submitted twice a year. Approved projects in Classes
A, B, C, and E continue to the end of the school year.
• In Class D, projects can be proposed on rapidly-developing studies that needto perform urgent and relatively large calculations. An approved projectcontinues for 6 months from its approval.
• Class S is for projects that are considered extremely important for the ﬁeldof condensed matter physics and requires extremely large-scale computation.
The project may be carried out either by one research group or cooperatively
by several investigators at diﬀerent institutions. A project of this class should
be applied with at least 10,000 points; there is no maximum. We requiregroup leaders applying for Class S to give a presentation on the proposal to
the Steering Committee of the SCC-ISSP. Class S projects are carried out
within one year from its approval.
• Project leaders can apply for points so that the points for each system do
not exceed the maximum point shown in this table.

2

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Table 1: Classes of research projects in SY 2022. Total points listed in this table
are rounded. In Class D, we collect information about the projects ended in eachsemester.
Class

A
B
C
D
E
S
SCCMS
Total

Maximum
PointsSys–B Sys–C
100
50
800
100
8k
500
10k
500
24k
1.5k
–
–

Application

# of
Proj.

any time
twice a year
twice a year
any time
twice a year
twice a year

28
94
176
6
13
0
20
337

Total points
AppliedApproved
Sys–B Sys–C Sys–B Sys–C
2.8k
1.4k
2.8k
1.4k
65.9k
6.7k
38.6k
5.5k
1092.9k 47.4k 566.8k 35.7k
28.5k
1.2k
23.0k
1.0k
273.0k 18.0k 185.5k 14.4k
0
0
0
0
5.2k
52.3k
5.2k
52.3k
1515.4k 79.9k 869.0k 63.2k

In addition, from SY 2016, ISSP Supercomputer has been providing 20% of
its computational resources for Supercomputing Consortium for Computational
Materials Science (SCCMS), which aims at advancing parallel computations incondensed matter, molecular, and materials sciences on the 10-PFlops K Computer
and the exascale post-K project. From SY 2020, up to 10% of the computational
resources have been provided for SCCMS. Computational resources have also beenallotted to Computational Materials Design (CMD) workshops, as well as CCMS
hands-on workshops.

1.3

Committees

In order to fairly manage the projects and to smoothly determine the system
operation policies, the Materials Design and Characterization Laboratory (MDCL)of the ISSP has organized the Steering Committee of the MDCL and the Steering
Committee of the SCC-ISSP, under which the Supercomputer Project Advisory
Committee (SPAC) is formed to review proposals. The members of the committees
in SY 2022 were as follows:

Steering Committee of the MDCLKAWASHIMA, Naoki
HIROI, Zenji
OZAKI, Taisuke
NOGUCHI, Hiroshi
UWATOKO, Yoshiya
OKAMOTO, Yoshihiko
SUGINO, Osamu
KUBO, Momoji

ISSP (Chair person)
ISSP
ISSP
ISSP
ISSP
ISSP
ISSP
Tohoku Univ.

3

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

HUKUSHIMA, Koji
HONDA, Fuminori
SHIMAKAWA, Yuichi
MOTOME, Yukitoshi
HOSHI, Takeo
ISHIWATA, Shintaro
HASEGAWA, Masashi
NAKATSUJI, Satoru

Univ. of Tokyo
Kyushu Univ.
Kyoto Univ.
Univ. of Tokyo
Tottori Univ.
Osaka Univ.
Nagoya Univ.
ISSP

Steering Committee of the SCC-ISSP
KAWASHIMA, Naoki
NOGUCHI, Hiroshi
OZAKI, Taisuke
SUGINO, Osamu
TSUNETSUGU, Hirokazu
KATO, Takeo
KIMURA, Takashi
MORITA, Satoshi
FUKUDA, Masahiro
IDO, Kota
KAWAMURA, Mitsuaki
NAKAJIMA, Kengo
OTSUKI, Junya
MOTOME, Yukitoshi
ONO, Tomoya
TODO, Synge
KUBO, Momoji
OBA, Fumiyasu
WATANABE, Hiroshi
HUKUSHIMA, Koji
KITAO, Akio
HAMADA, Ikutaro
YOSHIMI, Kazuyoshi
YATA, Hiroyuki
FUKUDA, Takaki

ISSP (Chair person)
ISSP
ISSP
ISSP
ISSP
ISSP
ISSP
ISSP
ISSP
ISSP
ISSP
Univ. of Tokyo
Okayama Univ.
Univ. of Tokyo
Kobe Univ.
Univ. of Tokyo
Tohoku Univ.
Tokyo Tech.
Keio Univ.
Univ. of Tokyo
Tokyo Tech.
Osaka Univ.
ISSP
ISSP
ISSP

Supercomputer Project Advisory Committee
KAWASHIMA, Naoki
OZAKI, Taisuke
NOGUCHI, Hiroshi
SUGINO, Osamu
TSUNETSUGU, Hirokazu
KIMURA, Takashi

ISSP (Chair person)
ISSP
ISSP
ISSP
ISSP
ISSP

4

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

KATO, Takeo
MORITA, Satoshi
FUKUDA, Masahiro
IDO, Kota
KAWAMURA, Mitsuaki
NAKAJIMA, Kengo
MOTOME, Yukitoshi
TODO, Synge
KUBO, Momoji
OBA, Fumiyasu
WATANABE, Hiroshi
HUKUSHIMA, Koji
ONO, Tomoya
OTSUKI, Junya
KITAO, Akio
HAMADA, Ikutaro
IKUHARA, Yuichi
SHIBATA, Naokazu
AKAGI, Kazuto
YANASE, Yoichi
HATSUGAI, Yasuhiro
OKADA, Susumu
KOBAYASHI, Nobuhiko
NAKAYAMA, Takashi
HOTTA, Takashi
MATSUKAWA, Hiroshi
YAMAUCHI, Jun
HAGITA, Katsumi
KONTANI, Hiroshi
SAITO, Mineo
YUKAWA, Satoshi
SUGA, Seiichiro
YASUDA, Chitoshi
KIM, Kang
MORIKAWA, Yoshitada
KOGA, Akihisa
SHIMOJO, Fuyuki
TAKETSUGU, Tetsuya
TSURUTA, Kenji
HAMAGUCHI, Satoshi
NISHIDATE, Kazume
KAGESHIMA, Hiroyuki
ISHII, Fumiyuki
TATETSU, Yasutomi
YANAGISAWA, Susumu

ISSP
ISSP
ISSP
ISSP
ISSP
Univ. of Tokyo
Univ. of Tokyo
Univ. of Tokyo
Tohoku Univ.
Tokyo Tech.
Keio Univ.
Univ. of Tokyo
Kobe Univ.
Okayama Univ.
Tokyo Tech.
Osaka Univ.
Univ. of Tokyo
Tohoku Univ.
Tohoku Univ.
Kyoto Univ.
Univ. of Tsukuba
Univ. of Tsukuba
Univ. of Tsukuba
Chiba Univ.
Tokyo Metropolitan Univ.
Aoyama Gakuin Univ.
Keio Univ.
National Defense Academy
Nagoya Univ.
Kanazawa Univ.
Osaka Univ.
Univ. of Hyogo
Univ. of the Ryukyus
Osaka Univ.
Osaka Univ.
Tokyo Tech.
Kumamoto Univ.
Hokkaido Univ.
Okayama Univ.
Osaka Univ.
Iwate Univ.
Shimane Univ.
Kanazawa Univ.
Meio Univ.
Univ. of the Ryukyus

5Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of TokyoSHUDO, Ken-ichi
Yokohama Natl. Univ.
OHMURA, Satoshi
Hiroshima Inst. Tech.
NOGUCHI, Yoshifumi
Shizuoka Univ.
NAKAMURA, Kazuma
Kyushu Inst. Tech.
GOHDA, Yoshihiro
Tokyo Tech.
RAEBIGER, Hannes
Yokohama Natl. Univ.
KAWARABAYASHI, Tohru Toho Univ.
KATO, Yusuke
Univ. of Tokyo
NASU, Joji
Tohoku Univ.
HOTTA, Chisa
Univ. of Tokyo
ISOBE, Masaharu
Nagoya Inst. Tech.
HARADA, Ryuhei
Univ. of Tsukuba
TANAKA, Shu
Keio Univ.
KOBAYASHI, Kazuaki
NIMS
TATEYAMA, Yoshitaka
NIMS
TAMURA, Ryo
NIMS
TADA, Tomofumi
Kyushu Univ.
HATANO, Naomichi
Univ. of TokyoYOSHINO, Hajime
Osaka Univ.
OKUMURA, Hisashi
NINS-ExCELLSHOSHI, Takeo
Tottori Univ.
TSUNEYUKI, Shinji
Univ. of TokyoSUZUKI, Takafumi
Univ. of Hyogo
YOSHIMOTO, Yoshihide Univ. of Tokyo
TOHYAMA, Takami
Tokyo Univ. of Sci.
ARITA, Ryotaro
Univ. of Tokyo
OGATA, Masao
Univ. of TokyoWATANABE, Satoshi
Univ. of Tokyo
YABANA, Kazuhiro
Univ. of Tsukuba
FURUKAWA, Nobuo
Aoyama Gakuin Univ.
KUROKI, Kazuhiko
Osaka Univ.
TANAKA, Yukio
Nagoya Univ.
KUSAKABE, Koichi
Univ. of Hyogo
SAKAI, Toru
Univ. of Hyogo
ISHIBASHI, ShojiAISTOTANI, Minoru
Univ. of Tsukuba
TOMITA, Yusuke
Shibaura Inst. Tech.
SHIRAISHI, Kenji
Nagoya Univ.
OGUCHI, Tamio
Osaka Univ.
KAWAKATSU, Toshihiro Tohoku Univ.
OTSUKI, Tomi
Sophia Univ.
ODA, Tatsuki
Kanazawa Univ.
ARAI, Noriyoshi
Keio Univ.
UNEYAMA, Takashi
Nagoya Univ.

6

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

1.4

Staﬀ

The following staﬀ members of the SCC-ISSP usually administrate the ISSP Supercomputer.
KAWASHIMA, Naoki
NOGUCHI, Hiroshi
OZAKI, Taisuke
SUGINO, Osamu
IDO, Kota
FUKUDA, Masahiro
KAWAMURA, Mitsuaki
MORITA, Satoshi
NAKANO, Hiroyoshi
YOSHIMI, Kazuyoshi
AOYAMA, Tatsumi
MOTOYAMA, Yuichi
YATA, Hiroyuki
FUKUDA, Takaki
ARAKI, Shigeyuki

Professor (Chair person)
Associate Professor
Professor
Professor
Research Associate
Research Associate
Research Associate
Research Associate
Research Associate
Project Researcher
Project Researcher
Technical Specialist
Technical Specialist
Technical Specialist
Project Academic Specialist

2

STATISTICS (SCHOOL YEAR 2022)

2.1

System and User Statistics

In the following, we present statistics for operation time taken in the period from
April 1st 2022 to April 3rd 2023 (SY 2022). In Table 2, we show general statisticsof the supercomputer system in SY 2022. The total numbers of compute nodes in
System B “ohtaka” and System C “kugui” are 1688 and 136, respectively. Consumed disk points amount to about a few percent of the total consumed points inboth System B and System C.
In Fig. 2, availabilities, utilization rates, and consumed points in Systems B
and C are plotted for each month. Throughout the school year, the availability
and the utilization rates were very high : the availability and the utilization rates
exceed about 90% and 80% throughout most of the year, respectively.
The user statistics are shown in Fig. 3. The horizontal axis shows the rank of theuser/group arranged in the descending order of the execution time (hour×nodes).
The execution time of the user/group of the ﬁrst rank is the longest. The verticalaxis shows the sum of the execution time up to the rank. From the saturation
points of the graphs, the numbers of “active” users of Systems B and C are around
400 and 100, respectively. The maximum ranks in the graphs correspond to thenumber of the users/groups that submitted at least one job.

7

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Table 2: Overall statistics of SY 2022

total service time (×103 node·hours)
number of executed jobs
total consumed points (×103 point)
CPU points (×103 point)
disk points (×103 point)
total exec. time (×103 node·hours)
availability
utilization rate

2.2

System B
ohtaka14420
476439
502
489
13
13293
96.60%
92.3%

System C
kugui
976
1088681818
1790
97.38%
80.98%

Queue and Job Statistics

Queue structures of Systems B and C in SY 2022 are shown in Tables 3 and 4,
respectively. In System B “ohtaka”, users can choose from two types of computenodes; jobs submitted to queues with “cpu” and “fat” at the end of their queue
names are submitted to CPU and Fat nodes, respectively. In System C, there are
two types of compute nodes: CPU and ACC nodes. Jobs submitted to queues with“cpu” and “acc” at the end of their queue names are submitted to CPU and ACC
nodes, respectively, If users submit a job to queues with “accs” at the end of theirqueue names, the job runs in the ACC node shared by other jobs of “accs“ queues.
See Sec. 1.1 for a description of each type of compute node. The user then has
to choose the queue according to the number of nodes to use and the duration of
their calculation jobs. Queue names starting with “F” are for jobs taking 24 hours
or less, while those starting with “L” can run much longer up to 120 hours. Morenodes are allotted to “F” queues in order to maximize the turnaround time of user
jobs. The queue names starting with “i” are used for interactive debugging of user
programs and the elapsed time limit is 30 minutes. The number following “F”,
“L”, or “i” correspond to the number of nodes that can be used by one user job.
Although we do not mention here in detail, to promote utilization of the massively
parallel supercomputer, background queues (queue name starting with “B”) for
Systems B and C which charge no points for the jobs have also been open.
To prevent overuse of the storage, points are charged also for usage of disk
quota in the three systems, as shown in Table 5. Disk points are revised often for
optimal usage of the resources by examining usage tendencies each year.
The number of jobs, average waiting time, and total execution time in eachqueue are shown in Tables 6 and 7. In Systems B and C, a large portion of jobs
have been executed in “F” queues. The largest amount of the execution time has
been consumed in the large-scale “F72cpu” and “F16cpu” queues for Systems B
and C, respectively. However, substantial number of jobs were run in every queue,
suggesting that a wide variety of user needs are met by this queuing scheme. In

8Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

System B

System C
Availability, Utilization [%]

100

90
80
Availability
Utilization

70

80
60

60

20

80

6

60
40
20
0
04

05

06

07

08

09 10
Month

11

12

01

02

4

2

0
04

03

Availability
Utilization

40

Point consumption[×103 points]

Point consumption[×103 points]

Availability, Utilization [%]

100

05

06

07

08

09 10
Month

11

12

01

02

03

Figure 2: Availabilities, utilization rates and point consumptions of each month
during SY 2022.

System B

System C
0.8

12
0.7
10

106 node·hours

106 node·hours

0.6
8

6

0.5

0.4
4
0.3
user exec. timegroup exec. time2

user exec. timegroup exec. time
0.2

0

100

200 300 400 500
Rank of user/group

600

700

0

20

40

60
80
100
Rank of user/group

120

140

Figure 3: User statistics. The horizontal axis shows the rank of the user/grouparranged in the descending order of the execution time (hour×nodes). The verticalaxis shows the sum of the execution time up to the rank.

9Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Table 3: Queue structures of System B in SY 2022

queue
name
F1cpu
L1cpu
F4cpu
L4cpu
F16cpuL16cpuF36cpuL36cpuF72cpuL72cpuF144cpu
L144cpu
i8cpu
F2fat
L2fat
i1fat

Elapsed time
limit (hr)
24
120
24
120
24
120
24
120
24
120
24
120
0.5
24
120
0.5

System B, ohtaka# of nodes # of nodes/job/queue
1
600
1
300
2–4
216
2–4
108
5–16
288
5–16
144
17–36
72
17–36
36
72
576
72
288
144
432
144
144
1–8
72
1–2
7
1–2
3
1
1

Memorylimit (GB)230/node
230/node
230/node
230/node
230/node
230/node
230/node
230/node
230/node
230/node
230/node
230/node
230/node
2900/node2900/node2900/nodejob points/(node·day)
1
1
1
1
1
1
1
1
1
1
1
1
1
4
4
4

most of these queues, the queue settings meet the user’s tendencies in that thewaiting times are on the order of the elapsed-time limit.

2.3

Project for Advancement of Software Usability in Materials Science

From School Year 2015, the supercomputer center (SCC) has started “Project
for advancement of software usability in materials science”. In this project, forenhancing the usability of the supercomputer system in ISSP, we perform some
software-advancement activity such as implementing a new function to an existing
code, releasing a private code on Web, writing manuals. Target programs are
publicly oﬀered in December and selected in the review by the Steering Committeeof SCC. The projects are carried out by the software development team composedof three members in ISSP. In SY 2022, three projects were selected as listed inTable 8.

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Table 4: Queue structures of System C in SY 2022

queue
name
i2cpu
F1cpu
L1cpu
F4cpu
L4cpu
F16cpuL16cpuF1accsL1accsi1accs
F2acc
L2acc
∗

System C, kugui
Elapsed time # of nodes # of nodes
limit (hr)/job/queue
0.5
1–2
8
120
24
1
120
1
60
24
2–4
32
120
2–4
16
24
5–16
64
120
5–16
32
24
1
3
120
1
1
0.5
1
1
24
1–2
4
120
1–2
2

Memorylimit (GB)240/node
240/node
240/node
240/node
240/node
240/node
240/node
60×NGPU
60×NGPU
60×NGPU
240/node
240/node

job points/(node·day)
1
1
1
1
1
1
1
1
1
1
1
1

NGPU in ACC queues denotes the number of occupied GPUs.

2.4

ISSP Data Repository

From School Year 2021, the supercomputer center (SCC) has started to operate
ISSP Data Repository (ISSP-DR) for accumulating and utilizing research data inmaterials science. GitLab is used as the data management system, and a portalsite is provided as a data registration and search system for the registered data.
By using ISSP-DR, it is possible to store and publish research data used in papers and datasets useful in the ﬁeld of condensed matter science. Users of ISSP
Supercomputer are welcome to apply for and use ISSP-DR.

Acknowledgments
The staﬀs would like to thank Prof. Takafumi Suzuki (now at University of Hyogo)
for developing WWW-based system (SCM: SuperComputer Management System)
for management of project proposals, peer-review reports by the SPAC committee,
and user accounts. We also thank Ms. Reiko Iwafune for creating and maintaining
a new WWW page of the ISSP Supercomputer Center.

11

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Table 5: Disk points of Systems B and C

System B ohtaka
System C kugui

∗
∗

/home
/work
/home
/work

point/day
0.001 × θ(q − 600)
0.0001 × θ(q − 6000)
0.001 × θ(q − 150)
0.0001 × θ(q − 1500)

q is denoted in unit of GB.
θ(x) is equal to the Heaviside step function H(x) multiplied by x, i.e., xH(x).

Table 6: Number of jobs, average waiting time, total execution time, and average
number of used nodes per job in each queue of System B.

queue# of Jobs

F1cpu
L1cpu
F4cpu
L4cpu
F16cpuL16cpuF36cpuL36cpuF72cpuL72cpuF144cpu
L144cpu
i8cpu
F2fat
L2fat
i1fat

160175
4726
77846
4400
44145
1990
1179
102
8509
101
3422
114
143548
5698
508
1003

System B, ohtaka
Waiting Time
Exec. Time # of nodes
3(hour) (×10 node·hour)
31.64
290.451.00
63.51
207.391.00
26.01
1203.73
2.82
58.22
493.382.89
12.77
1586.639.23
58.49
661.299.29
52.79
311.3528.75
69.71
115.5022.46
19.76
3711.88
72.00
40.95
270.2772.00
18.28
2090.58
144.00144.00257.46581.50
0.09
95.25
4.28
206.85
36.39
1.24
60.36
17.51
1.21
0.35
0.09
1.00

12

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Table 7: Number of jobs, average waiting time, total execution time, and average
number of used nodes per job in each queue of System C.

queue# of Jobs

i2cpu
F1cpu
L1cpu
F4cpu
L4cpu
F16cpuL16cpui1accsF1accsL1accs
F2acc
L2acc

6417
32465
862
3780
374
1837
83
3794
2946
418
3225
72

System C, kugui
Waiting Time
Exec. Time # of nodes
3(hour) (×10 node·hour)
0.39
0.84
1.30
3.03
92.09
1.00
24.34
31.42
1.00
6.13
80.44
3.31
35.69
45.22
3.44
12.60
134.95
13.53
16.11
23.83
12.24
0.00
0.20
1.00
2.88
10.41
1.00
1.16
1.57
1.00
5.96
4.16
1.04
2.05
0.63
1.29

Table 8: List of Project for advancement of software usability in materials science
for SY 2022.
Project Proposer
Shusuke Kasamatsu
Yamagata University
Akito Kobayashi
Nagoya University

Project Name
Coupling of extended ensemble sampling
and ﬁrst-principles solvers
Advancement of eﬀective model solvers
toward high-throughput calculations― Wavenumber based mean-ﬁeld approximation
and random phase approximation

13

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

3
3.1

RESEARCH REPORTS
Invited Articles

Training method for reﬁnement of machine-learninginteratomic potential and its applications

15

Kohei SHIMAMURA, Akihide KOURA, and Fuyuki SHIMOJOFirst principles calculation of copper-oxide superconductors
exhibiting electron fractionalization

23

Masatoshi IMADA

Molecular dynamics study of ultra-long chain fatty acidin lipid bilayer
Kazutomo KAWAGUCHI and Hiroshi NOGUCHI

14

35

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Training method for reﬁnement of machine-learninginteratomic potential and its applications
Kohei Shimamura, Akihide Koura, and Fuyuki Shimojo
Department of Physics, Kumamoto University,
Kumamoto 860-8555, Japan

Abstract
Machine-learning interatomic potential (MLIP) isbased on low-cost ML models trained with ﬁrstprinciples data to achieve high accuracy in molecular dynamics simulations, expanding the range ofapplicable time-space domains. However, the current widely used training method has limitationsin accurately estimating stress and fundamental
physical quantities such as free energy and thermal conductivity. We have therefore emphasized
the importance of stress training for MLIPs andinvestigated other issues behind the current training method. The report discusses the role of stresstraining in reproducing solid-liquid phase transitions and estimating thermal conductivity, as wellas the effect of training data with different k points
on free energy.

ing on the reﬁning the training method of MLIPs.
In particular, we have emphasized the importanceof stress training to MLIP [4, 5]. Recent mainstream training methods set total potential energy
and atomic force as the targets in training [6]. Despite the fact that the accuracy of stress is not guaranteed, some previous studies to investigate highpressure phenomena have utilized such MLIPs. It
is moreover found that the stress training is necessary to accurately estimate fundamental physical quantities such as thermal conductivity (TC),and becomes one of the indispensable training
conditions [5]. This report discusses the importance of stress training of MLIPs for reproducing aphase transition and estimating TC. In addition, we
also discuss the effect of training data with different k points on thermodynamic quantities obtained
through MLIPs, such as free energy. As the MLIP,
we adopted an interatomic potential using an artiﬁcial neural network (ANN potential).

1 Introduction
The machine-learning interatomic potential(MLIP) is used in a wide range of researchﬁelds. By training low-cost machine learningmodels with ﬁrst-principles molecular dynamics(FPMD) data, the MLIP achieves high accuracy,
signiﬁcantly expanding the applicable space-time
domain of MD simulations. MLIP is used to
calculate physical quantities that require large
statistical amounts, to investigate phenomena
occurring in long-term dynamic simulations
such as shock waves, and to explore reaction
pathways [1, 2, 3].
Under these circumstances, we have been work-

1.1

Stress training

Originally MLIPs were trained with only total potential energy. Afterward, atomic force, which is
crucial for MD simulations, was integrated into
the training process, leading to a signiﬁcant improvement in accuracy compared to MLIPs that
only consider the total potential energy. Recently,
some MLIPs have been obtained by training with
FP data of the total potential energy, atomic force,and stress. However, such MLIPs that target stressin training may still be in a minority. In this report,
we show our results that the accuracy of stress is
not guaranteed by training with the total potential

15

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

energy and atomic force alone, and that the solidliquid phase transition of Na is accurately reproduced by training with stress [4].

1.2Free energy calculation

Thermodynamic integration (TI) is a popularmethod for computing free energy, but when combined with MLIPs such as ANN potentials, someconsiderations need to be taken for liquids. We
discovered a method that involved two steps, using an ideal gas and a soft potential as reference
systems [7]. We show how to calculate the free energies of solid and liquid states by the TI method
using ANN potential for Na [4].
Furthermore, in numerous previous studies,
MLIPs were trained using FP data with multiple
k points instead of using of large-scale systems.
We however show that as precision requirements
increase, larger systems with a greater number of kpoints will be necessary for the free energy calculation [4].

1.3

TC calculation

The TC calculation method based on the GreenKubo (GK) formula has been widely used because
it can be applied to even disordered systems. However, MLIPs including the ANN potential belong
to many-body potentials, and the many-body effects have to be taken into account in the formulaof the heat ﬂux [8]. We found that using the rigorous heat ﬂux formula considering the effects together with applying stress training to the ANN
potential led to highly accurate TC estimates [9].
Here, we show the result of the TC for the superionic conducting Ag2 Se (α -Ag2 Se) by homogeneous nonequilibrium MD (HNEMD) based on the
GK formula [9].

2Computational Details

In this study, FPMD and MD simulations using MLIPs were conducted using the QXMD
code [10].

2.1

ANN potential

The ANN potentials were constructed usingænet [11], which is a training code developed byArtrith et al. The ANN potential comprises feed
forward neural networks (FFNNs) created for eachatomic species. The total potential energy U ANN
is deﬁned by the sum of the atomic potential energies {εiANN } output from FFNNs for all atoms inthe system [12].
Natom

U ANN = ∑ εiANN ,(1)iwhere Natom denotes the total number of atoms.
The atomic potential energy depends on the
structural descriptor used. Chebyshev descriptors [13] were used in this study. The atomic potential energy of the Chebyshev descriptor is only
a function of the relative coordinates ri j [5]()εiANN = εiANN {ri j } j̸=i .
(2)

2.2

Training methods

The following cost function C was used to train the
ANN potentials. It consists of three loss functions,
i.e., the total potential energy, atomic force, andvirial.
()2
pU 1 NI UIANN −UIRef
C =2 NI ∑
Natom
I
+

+

)2
pF 1 NI1 Natom ( ANN
FI,i − FRef∑∑
I,i
2 NI I 3Natom i()2
WI,ANN−WI,RefpW 1 NI 1 6
j
j
, (3)
∑
2 NI ∑NatomI 6 j

where NI denotes the number of training data. The
symbol “Ref” denotes the training data. Factor 6
for the virial term reﬂects the number of independent degrees of freedom of the virial tensor WANN.
I
Because the three loss functions differ in dimension and size, pU , pF , and pW are introduced as
adjustable parameters. With appropriate settingsof these parameters, highly accurate ANN potentials can be constructed [14].
Note that the training for virial tensor WANN
isI
ANN
equivalent to that for the stress tensor PI
calcuANN
lated by dividing WI
by supercell volume.

16

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

The ANN potential trained with total potential
energy and atomic force is called EF-ﬁt, while theone trained including virial is called EFP-ﬁt.

2.3

Generation of training data on Na

We obtained the training data of Na by running
FPMD simulations. We calculated the electronic
states using the projector-augmented-wave (PAW)
method [15] within the framework of density functional theory (DFT) [16], in which the generalized
gradient approximation (GGA) [17] was used forthe exchange-correlation energy. The plane-wave
cutoff energies were 10 and 50 Ry for the electronic pseudo-wave functions and pseudo-charge
density, respectively. Projector functions weregenerated for the 3s, 3p, and 3d state of Na atom.
The FPMD simulations were mainly performed
using 128 Na atoms with periodic boundary conditions. For the Brillouin-zone sampling, 4 and32 special k points were utilized in addition to theΓ point. These are equivalent to sampling at theΓ point in systems consisting of 1024 and 8192
atoms, respectively. For comparison, FPMD simulations were also conducted on 1024 Na atoms
using the Γ point. The equations of motion weresolved under the isothermal-isobaric (NPT ) ensemble through an explicit reversible integrator[18]. The pressure was set to be ambient. The MD
time step ∆t was 2.9 fs. The simulation temperature was mainly set to 350 K. In addition, FPMD
data using 128 atoms with Γ point were preparedfor heating and cooling processes in the range between 450 and 200 K to investigate whether the
ANN potential can reproduce the melting and solidiﬁcation processes of Na.

2.4

TI method

In the TI method [4, 7], the interaction energyU(λ ) with parameter λ and the Helmholtz free energy F(λ ) are connected by the following relation:∂ F(λ )=∂λ⟨∂ U(λ )
∂λwhere ⟨· · · ⟩NV T denotes the canonical (NV T ) ensemble average. U(λ ) is given asU(λ ) = λ UNa + (1 − λ )Uref (0 ≤ λ ≤ 1),

where UNa and Uref are the total potential energiesof the target (Na) and reference systems, respectively. By substituting Eq. (5) into Eq. (4), andintegrating both sides with respect to λ from 0 to
1, we obtain the following formula:F(λ = 1) − F(λ = 0) =∫ 1
0⟨UNa −URef ⟩NV T d λ .

(6)F(λ = 1) and F(λ = 0) are the free energies of
the target and reference systems, respectively. As
reference systems for the solid and liquid phases,
we employed an Einstein solid and an ideal gas,
respectively.
In the liquid phase, the soft core (SC) potential USC , deﬁned as follows, was employed to
avoid shortening the interatomic distance as λ approaches zero [7].
N

USC ({rr i }) = ∑ µ
i< j

(

σ
ri j

)n
.

(7)

The parameters were used to be µ = 7.92 ×
10−4 hartree, σ = 6.752 bohr, and n = 12. The
Helmholtz free energy in the liquid phase was calculated by two TIs. The ﬁrst TI is calculated along
the ideal gas to SC liquid and the second one isfrom SC to the target liquid Na. In addition, to
integrate the ﬁrst while preventing divergence atsmaller λ , we adopted the coordinate transformation from λ to x proposed in the previous study
[19]
(
) 1
x + 1 1−kλ (x) =,
(8)2
where the interval of integration is changed from0 ≤ λ ≤ 1 to −1 ≤ x ≤ 1. We employed the parameter k = 0.85. Applying this transformation to
the integration with respect to λ , we have the following formula:
∫ 1

⟩
,

(5)

0

(4)

NV T

17

f (λ )d λ =12(1 − k)∫ 1
−1

f (λ (x))λ (x)k dx .
(9)Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

2.5

Generation of training data on α Ag2 Se

We generated training data using MD simulations
with an empirical interatomic potential (EIP) instead of FPMD. One reason for this is that we
can obtain a reference value of TC. The other reason is that the EIP of Ag2 Se proposed by Rino etal. [20] is a physically excellent EIP that can describe phase transitions among nonsuperionic, superionic conducting, and liquid phases [21]. The
training data were obtained by MD simulation with
the NV T ensemble at 500 K for the α -Ag2 Se system that comprises 256 Ag + 128 Se atoms in experimental density [22].

the heat ﬂux formula and showed that the atomic
virial is expressed as follows.
WANN-I= ∑ ri j ⊗ij̸=i

2.6.1 GK formula and heat ﬂux
According to the GK formula [23, 24], the αβcomponent of the TC tensor καβ can be estimatedas (α , β ∈ {x, y, z})

καβ

=

Ω
kB T 2∫ ∞0

WANN-II= ∑ ri j ⊗ij̸=iwhere T , kB , and Ω are temperature, the Boltzmann constant, and volume of the supercell, respectively. ⟨JQ,α (t)JQ,β (0)⟩ denotes the autocorrelation function of the heat ﬂux JQ , and thefollowing expression is employed for both the EIP
and ANN potentials:
JQ =,∂ εiANN,∂ r ji[

1 Natom
1 Natom
1 Natom
ε
t
v
+
v
+
Wi vi
i i
i i
Ω ∑
Ω ∑
Ω ∑
i
i
i
(11)

where ti , vi , εi , and Wi are atomic kinetic energy,
velocity, potential energy, and virial tensor for the
ith atom, respectively.
2.6.2 Atomic virials in heat ﬂux formula
To derive a rigorous formula of the heat ﬂux forthe many-body potentials, an expression for theatomic potential energy is required [8], which corresponds to Eq. (2) for the ANN potentials. In ourprevious study [5], we have succeeded in deriving(13)∂ εiANN ∂ ε j
ri j ⊗+∑
2 j̸=i∂ r ji∂ r ji1
WANN-III
=
i

ANN

]
. (14)

WANN-II
appears naturally in the stress-tensor
iderivation for the ANN potentials [25].
In
Ref. [26], WANN-III
wasusedbecause
the
pairi
[
]
wise force deﬁned as FANN=−ij

⟨JQ,α (t)JQ,β (0)⟩dt, (10)(12)

Incidentally, for the many-body potentials suchas the ANN potential, there are countless deﬁnitions of atomic virial which give identical total
virials [9, 5]. We deﬁne two more apparently reasonable atomic virials used for heat ﬂuxes in theprevious studies.

and

2.6 TC calculation∂ ε ANNj.
∂ r ji∂εj∂ εiANN∂ r ji + ∂ r ji

ANNsatisﬁes Newton’s third law [8]. The summation
Nis equal to FANN
. While the mathe∑ j atom FANN
ij
imatical properties of the three atomic virials differ, their sums over Natom yield the same total virial
WANN . Hereinafter, the heat ﬂux formulae deﬁned
by substituting WANN-I, WANN-II, and WANN-IIIiiiI
into Eq. (11) are referred to as JQ , JIIQ , and JIII
Q , respectively. Some studies used ANN potentials that
estimated TCs with JIIQ [27] and JIII
Q [26]. Recent
studies highlighted that the atomic virial can signiﬁcantly impact the TC when dealing with manybody potentials [8, 28, 29]. It is crucial to compare
the TCs obtained from these heat ﬂux formulae.
2.6.3

HNEMD method

The HNEMD method [30], which is based on the
GK formula, has successfully reduced the computational cost by enabling the calculation of TC using the time average of the heat ﬂux instead of integrating its auto-correlation function.

κ=

18

Ω

lim ⟨JQ,x ⟩t ,Fext T t→∞(15)

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

where Fext denotes a magnitude of perturbation
along the x direction of the system. Fext = 0.01
bohr−1 was selected for the HNEMD simulationsin this study [5].

Results

3.1

Solid-liquid phase transition of Na

We ﬁrst investigated whether the EF-ﬁt and EFPﬁt models reproduced the hysteresis of melting andsolidiﬁcation in Na [4]. These two types of ANN
potentials were constructed from the FPMD data inthe temperature range of 200 to 450 K, performed
using 128 atoms with Γ point as mentioned in section 2.3. The EFP-ﬁt was able to accurately reproduce the heating and cooling processes of FPMD.
The temperature dependence of radial distributionfunction g(r) obtained from the MD simulations
is shown in Fig. 1. g(r)’s by the EFP-ﬁt (green
dashed lines) are quite consistent with the FPMD
results (black solid lines). In contrast, the g(r) obtained from the MD simulations using EF-ﬁt (reddashed lines) showed a signiﬁcant deviation fromthose obtained from FPMD . The above results
demonstrate the risk by using MLIPs such as EFﬁt, which do not guarantee the accuracy of stress.

3.2

Size dependence of the free energy ofNa

In many previous studies, the training data of
MLIPs were created by FP calculation with manyk points to avoid using large-scale systems. However, it is not obvious whether the respective
MLIPs constructed from the training data obtainedwith a speciﬁc number of k points and corresponding system sizes show equivalent accuracy. Therefore, we conducted the following analysis to clarify the inﬂuence of training conditions such as system size and the number of k points on thermodynamic quantities such as free energy [4].
We created the four types of EFP-ﬁt models.
These were trained with the FPMD data obtained
from the 128-atom system with Γ point, 128-

heating

400 K

FPMD
EF-fitEFP-fit350 K

cooling

2
0
4
2

g(r)

3

4

0
4

300 K

2
0
4

250 K

2
0

2

4

6

2

4

6

8

r [Å]

Figure 1: Temperature dependence of the radial
distribution function g(r) in the heating and cooling processes. The black solid lines correspond to
the results from FPMD simulations. The red andgreen dashed lines correspond to those from MD
simulations with EF-ﬁt and EFP-ﬁt, respectively.
Taken from Ref. [4].

atom system with 4k points, 128-atom systemwith 32k points, and 1024-atom system with Γpoint, which are hereinafter referred to as 128ΓANN, 128-4kANN, 128-32kANN, and 1024ΓANN potentials, respectively. To investigate thesize-dependent behavior of thermodynamic quantities, we calculated the Helmholtz free energy
for 128 and 1024 atoms using 128-ΓANN and
128-4kANN potentials. Additionally, for comparison, we computed the same quantities for 1024
atoms using 1024-ΓANN and 128-32kANN potentials. Figure 2 shows the (a) Helmholtz free energy F, (b) total potential energy U, and (c) product of entropy and temperature T S as a functionof the number of atoms N. The triangles and circles represent the solid and liquid phases, respectively. The black, red, green, and blue symbols de-19

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of TokyoF [eV/atom]

note the values calculated using the 1024-ΓANN,
128-4kANN, 128-ΓANN, and 128-32kANN potentials, respectively. As observed from this ﬁgure,the outcomes obtained using the128-ΓANN potential exhibit signiﬁcant deviation from the others.
Moreover, the values computed using the 1024ΓANN, 128-4kANN, and 128-32kANN potentials
are nearly identical with regard to the vertical axis.
-391.86(a)-391.88(b)

U [eV/atom]-391.68-391.70-391.72∆F between 128 and 1024 atoms is merely 0.13
and 0.11 meV/atom when calculated with the 128ΓANN and 128-4kANN, respectively, suggestingthat for systems with more than 128 atoms, the size
dependence on ∆F is almost negligible in comparison with the difference of Helmholtz free energy;(iii) the difference between the ∆F values computed using the128-4kANN and 1024-ΓANN potentials is relatively small, with a difference of only
0.50 meV/atom for 1024 atoms; (iv) the value of∆F obtained using the 128-32kANN is more similar to that obtained using the 1024-ΓANN than tothat obtained using the 128-4kANN. Despite being
trained with FPMD data at a single temperature of
350 K, the 128-4kANN potential is capable of accurately calculating ∆F with an error of only a few
tenths of meV/atom. However, if higher precision
is required, larger systems with more k points will
be necessary.

TS [eV/atom](c)1024-GANN
128-4kANN
128-GANN
128-32kANN

0.18
0.16
0

500

N

Table 1: Differences of Helmholtz free energy∆F = Fliq − Fsol between the solid and liquid statesat 350 K in meV/atom. Taken from Ref. [4]Atoms 128-Γ 128-4k 1024-Γ 128-32k
128
1024

1000

Figure 2: (a) Helmholtz free energy F, (b) total
potential energy U, and (c) product of entropy andtemperature T S obtained using the TI method at
350 K as a function of the number of atoms N.
Triangles and circles correspond to the solid andliquid states, respectively. The black, red, green,
and blue symbols indicate the values calculatedusing the 1024-ΓANN, 128-4kANN, 128-ΓANN,
and 128-32kANN potentials, respectively. Taken
from Ref. [4].
To further examine the size dependence, we
computed the differences in Helmholtz free energy between the solid and liquid states ∆F =Fliq − Fsol , as shown in Table 1. From the table,
we obtained the following ﬁndings: (i) the values obtained from the 128-ΓANN potential differs largely from the others; (ii) the difference in

3.3

-2.47
-2.34

-0.63
-0.74

-0.24

-0.33

TC calculation of α -Ag2 Se

The stress training is also associated with the accuracy of TC. Here, we present the results of aninvestigation of the inﬂuence of the heat ﬂux formulae (JIQ , JIIQ , and JIII
Q ) and stress training on TC,
using α -Ag2 Se as the test system [5].
To examine the effect of stress training, we used
the EF-ﬁt and EFP-ﬁt models, and also took into
account the dependence of the initial weight parameters in ANN. We constructed ﬁve each of
the EF-ﬁt and EFP-ﬁt with different initial weightparameters. Figure 3 plots ﬁve TCs (κ ANN ) obtained from the respective EF-ﬁt and EFP-ﬁt models as a function of root mean square errors ofstress for the validation data (∆Pvalid ). The vertical axis shows the absolute value |κ ANN − κ EIP |,20

4

Summary

In this report, we have investigated and discussed
issues that are unveriﬁed with the current training
method of MLIPs. Speciﬁcally, we reported thatthe stress training is necessary to accurately reproduce the solid-liquid phase transition of Na and theTC of α -Ag2 Se. In addition, it was suggested thatin order to construct more accurate MLIPs fromthe viewpoint of free energy calculations, increasing the number of k points is not the only solution,
and a large-scale system is also necessary.
We would like here to comment on other unestablished aspects of MLIP training methods. It
is important to recognize that adjusting the coefﬁcients of the cost function can dramatically alter
the accuracy of MLIPs [14]. The heat ﬂux regu-

-1
-1
EIP

0.1

EF-fit withJIEF-fit with JIIIII
EF-fit with J
I
EFP-fit with J
II
EFP-fit with J
EFP-fit with JIII


ANN

-k

0.2

|k

where κ EIP is the reference TC obtained from theEIP (0.274 Wm−1 K−1 ). For the EF-ﬁt, while three
TCs calculated by JIQ are sufﬁciently close to 0,
all of the ﬁve ∆Pvalid are larger than 0.47 GPa.
Furthermore, |κ ANN − κ EIP | exhibits a large valueof ∼ 0.2 Wm−1 K−1 with higher values of ∆Pvalid
when using JIQ . JIIQ gives large errors of the TCs∼0.25 Wm−1 K−1 with higher ∆Pvalid . In the caseof the TC calculated with JIII
Q , the deviation in the
TC is larger than 0.02 Wm−1 K−1 . The plots in
Fig. 3 indicate that when ∆Pvalid is larger, errors in
TC become larger and sometimes result in signiﬁcant deviations.
The good agreement with the reference values
suggests that stress training plays a crucial role as
anticipated. For EFP-ﬁt, all |κ ANN − κ EIP | calculated with JIQ is plotted near the origin as shownin Fig. 3. There are no cases showing larger TC
errors as seen in the EF-ﬁt. The maximum valueof |κ ANN − κ EIP | is ∼0.005 W m−1 K−1 , which
is much smaller than even the minimum valuesof |κ ANN − κ EIP | calculated with JIIQ and JIII
Q , i.e.
−1−10.055 and 0.041 W m K , respectively.
From the above results, only JIQ gives a correct
estimation of TC. Further, the correct TC may not
be obtained even with JIQ unless ∆Pvalid is sufﬁciently minimized through stress training.

| (Wm K )Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

0.0
0.0

0.5

1.0

1.5

ΔPvalid (GPa)

2.0

Figure 3: Absolute difference between the TC calculated from the ANN potentials (κ ANN ) and theEIP (κ EIP ) as a function of root mean square errors
for the validation data (∆Pvalid ) of ﬁve ANN potentials belonging EF-ﬁt (ﬁlled circles) and EFPﬁt (cross marks) for the validation data. κ ANN
and κ EIP are calculated from HNEMD simulations.
κ ANN are computed using heat ﬂux formulae JIQ(black), JIIQ (red), and JIII
Q (green). Taken from
Ref. [5].

larization method [31] introduced into the costfunction to estimate partial TCs may also lead to
improved robustness of MLIPs. It should be emphasized that more attention should be paid to theimportance of the modiﬁcation of the cost function
for the improvement of MLIPs as well as the developments in their architectures and descriptors.

Ackwnowledgements
This study was supported by MEXT/JSPS
KAKENHI Grant Numbers Nos. 22K03454,
21H01766, and 19K14676, and JST CREST Grant
Number JPMJCR18I2, Japan. The authors thankthe Supercomputer Center, the Institute for Solid
State Physics, University of Tokyo for the use of
the facilities. The computations were also carriedout using the facilities of the Research Institute forInformation Technology, Kyushu University.

21

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

References[1] Morawietz T and Artrith N 2021 J. Comput.
Aided Mol. Des. 35 557–586[2] Misawa M, Fukushima S, Koura A, Shimamura K, Shimojo F, Tiwari S, Nomura K i,
Kalia R K, Nakano A and Vashishta P 2020
J. Phys. Chem. Lett. 11 4536–4541[3] Yang M, Bonati L, Polino D and Parrinello M
2022 Catal. Today 387 143–149[4] Irie A, Fukushima S, Koura A, Shimamura
K and Shimojo F 2021 J. Phys. Soc. Jpn 90
094603[5] Shimamura K, Takeshita Y, Fukushima S,
Koura A and Shimojo F 2021 Chem. Phys.
Lett. 778 138748[15] Kresse G and Joubert D 1999 Phys. Rev. B 59
1758–1775[16] Hohenberg P and Kohn W 1964 Phys. Rev.
136 B864[17] Perdew J P, Burke K and Ernzerhof M 1996
Phys. Rev. Lett. 77 3865–3868[18] Tuckerman M, Berne B J and Martyna G J
1992 J. Chem. Phys. 97 1990–2001[19] Rang M and Kresse G 2019 Phys. Rev. B
99(18) 184103[20] Rino J P, Hornos Y M M, Antonio G A,
Ebbsjö I, Kalia R K and Vashishta P 1988 J.
Chem. Phys. 89 7542–7555[21] Shimojo F and Okazaki H 1991 J. Phys. Soc.
Jpn. 60 3745–3753[6] Behler J 2021 Chem. Rev. 121 10037–10072[7] Fukushima S, Ushijima E, Kumazoe H,
Koura A, Shimojo F, Shimamura K, Misawa
M, Kalia R K, Nakano A and Vashishta P2019 Phys. Rev. B 100(21) 214108[8] Fan Z, Pereira L F C, Wang H Q, Zheng J C,
Donadio D and Harju A 2015 Phys. Rev. B
92(9) 094301[22] Okazaki H 1967 J. Phys. Soc. Jpn. 23 355–
360[23] Kubo R 1957 J. Phys. Soc. Jpn. 12 570–586[24] Green M S 1952 J. Chem. Phys. 20 1281–
1295[25] Behler J 2011 J. Chem. Phys. 134 074106[9] Shimamura K, Takeshita Y, Fukushima S,
Koura A and Shimojo F 2020 J. Chem. Phys.
153 234301[26] Mangold C, Chen S, Barbalinardo G, Behler
J, Pochet P, Termentzidis K, Han Y, Chaput
L, Lacroix D and Donadio D 2020 J. Appl.
Phys. 127 244901[10] Shimojo F, Fukushima S, Kumazoe H, Misawa M, Ohmura S, Rajak P, Shimamura K,
Bassman L, Tiwari S, Kalia R K, Nakano A
and Vashishta P 2019 SoftwareX 10 100307[27] Sosso G C, Donadio D, Caravati S, Behler J
and Bernasconi M 2012 Phys. Rev. B 86(10)104301[11] Artrith N and Urban A 2016 Comput. Mater.
Sci. 114 135 – 150[12] Behler J and Parrinello M 2007 Phys. Rev.
Lett. 98(14) 146401[13] Artrith N, Urban A and Ceder G 2017 Phys.
Rev. B 96(1) 014112[14] Irie A, Shimamura K, Koura A and Shimojo
F 2022 J. Phys. Soc. Jpn. 91 045002[28] Surblys D, Matsubara H, Kikugawa G andOhara T 2019 Phys. Rev. E 99(5) 051301[29] Boone P, Babaei H and Wilmer C E 2019 J.
Chem. Theory Comput. 15 5579–5587[30] Evans D J 1982 Phys. Lett. A 91 457 – 460[31] Shimamura K, Koura A and Shimojo F 2022
arXiv: 2101.10468

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo                                                    !"#$$$%& '        (&&)     &    *+ ,   -   ./#$$
  0 )   -' )     1'   2  (   #."/        #                                                                           "                                         $ "              %                                                           &                    '()*                            %                   +,                                                                                                   !                             -                                                 .                                                                                      "                                  

23

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo           -                                                    "     "              ""                                                                !  '/ 0                        1+2。
                        .   
"                  .                                                  "          "                           $                                      
%   Æ   3                              -        $                      %                          
4          !                      1' 62       1'2      708 9      
                : 4  708 %                            5    ;                           1,<=2           "                          943: 162      .                                "                     $             .       

24

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo      "    %        
9>4!?: %         E    F    F '          1)2
6  
9':    $                                 1+2     ?     0 @     00 @ 90'': 9       0   ?             ''AB                    : ? & 0 @                 ?66A' 9'A        9 :  
CA B: ? & 00 @    ?66'6    9  :     ? 
9),'AA B:  ? 0 @ 9(AB:            ? %       
%         0 @            ( 
          ! '     5             0 @         C                           1C2                 7   0                1( 'A2 ?                         0        0
      9D!: $   ;      %                                  ! 6       
         . ! +     
!   9"   ! 6:           9:          $        9?  :             "       "    0 ?            +  @ 6      0 @         Æ                 00 @  ?          $ G 9:   9:       &                                          !            G     G  ! +                E    9:    "     ?                  "                             ?                          ;      Æ             E G                 ! +     ! C           %         Æ E AA,A'                   .        "  ?          Æ  A'              ?                    $ 708    1+2             

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Energy (eV)(a) Bi2201 (δ=0.0)(c) Bi2212 (δ=0.0)(g) Ca11 (δ=0.0)

2

2

2

0

0

0

0

-2

-2

-2

-2

-4

-4

-4

-4

-6

-6

-6

-6

-8

-8
Γ

D

Z

Γ

X

-8
Γ

D

(b) Bi2201 (δ=0.2)

Energy (eV)(e) Hg1201 (δ=0.0)

2

Z

X

Γ

-8
Γ

(d) Bi2212 (δ=0.2)

D

Γ

X

Γ D X

(f) Hg1201 (δ=0.1)

2

2

2

2

0

0

0

0

-2

-2

-2

-2

-4

-4

-4

-4

-6

-6

-6

-6

-8

-8
Γ

D

Z

Γ

X

-8
Γ

D

Z

X

Γ

Γ

Z R A

Z

(h) Ca11 (δ=0.1)

-8
Γ

D

Γ

X

Γ D X

Γ

Z R A

Z

!  6/ 8        C            ;   1+2                         H

0.0

0.025
1/L

0.05

L = 24

P̄d

100

P̄d
|Pd (r)|

0.00

0.025
1/L

0.05

0.0

0.025
1/L

10−2

0.05

10−1

10

r

20

0

0.025
1/L

0.05

0.02

0.00

10−1

10
0.0

0.025
1/L

0.05
10

10

r

10−3

20

0

0.05

0.0

0.025
1/L

0.05

(h) δ = 0.247

−1

−2

0.025
1/L

100

0.005

0.000

0.0

10−2

(g) δ = 0.208
100

10−2
0

0.0

L = 36

10−2

0.01

0.00

0.00

10−1

(f) δ = 0.167

0.02

10−1

0.0

L = 3010−2(e) δ = 0.125
100

0.00

10−1

0.02

P̄d

10−2

0.02

(d) δ = 0.101
100

P̄d

P̄d

P̄d
|Pd (r)|

0.00

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(c) δ = 0.087
100

P̄d

(b) δ = 0.045
100

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P̄d

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100

0.0

10

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10

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!  +/ &            00 @              "         .     "      %                1C226

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo                                                                   .                               
                  !  C/        9Æ:                1C2        
;        6C  6C       =  (   C                          1C2           

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                            =     )9438&:       -       
9&7: 1'' '62                       A'               I                    
0.12
   1'+2@                     
0.08         
CaCuO         
0.04
Hg1201! ,          
Bi2212'AH        
Bi22010.00           78910L = 24
L→∞

FSC

0.15

0.1

P̄d
0.02

0.05 0.01

0.0
0.0
0.0
0.0

0.1

0.2

0.1

0.2

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2

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−0.1

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20

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0.44

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0.43

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0.0

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0.1

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0

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20

0.42

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0.41

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0

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo                                        %                  .              I                                        '6            !  =/ 3                                                       @         00 @          Æ E A'*=
           %                                                      E '    (                         -     %       1C2               .                                    E '6    CA J         00 @ A'* 96: @                          C         +AJ
           ! ) E  E '6       !   0                       $             '6                                      9Æ:                      ! (            %                  9!  %:                  Æ                                                                          7           9   :        !  ( 9:  9 :          Æ     
 E  F Æ F Æ F                                       Æ                                               /               
δ = 0.167

0.14

0.20

0.12

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0.15

0.1

1.0

0.10

1.25

1.5

0.05
0.00

H(α, 1.0)

0

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1

H(ξ, ξ)2α or ξ

H(1.2, ξ)34

Tcopt (K)Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo                                 .         9
 :                     9:         9:         $        90!7:                   /120∞
0.18|t1 |FSC

80

40

0
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Hg1201

Bi2212

Bi2201!  )/ 0          A'*                     3                     %         1C2 !  ?66A'  ?66'6
                     E 1 9:  F  9: 
F K9:9  F :2
F 9 9 : 
9+:F 9 :  F  9 :  :  9:   9:                  K9:   "                                                    
                 .         K                           @        K        "  %  "             %                 !  (/ Æ               "                      
00 @   9:                        9 : 1C2。. 9 :         "  Æ                        L 9:  L 9:           $                
Ftotal

Etotal /N

6543 


Ptotal

−0.05
−0.10

(c)

(d)

0.02

FU

0.30
0.25

0.00

PU

0.20

0.0

0.08 0.16 0.24
δ

0.0







0.00

EU /N − FU

EU /N

0.35

0.05

  

(b)

Etotal /N − Ftotal(a)

0.08 0.16 0.24
δ



29











Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo     0!7  
 E
1 9:  F  9: 
F K9:9  F :F 9L 9:  F L 9: F :2

                438&   1'=2           %           
0!7   1'*2 &    %                  "     $       
9C:      "   ;M           9            
:   0!7 9C:      438&     
%             ' 9,:         
 9 : E
   9:  N 9 :  9 :                
 'AAJ                                             4                      #  
9 : E  F  9N: F9N :9 : 9*: " 94.#&:                     0!7   $   438&      
N 9 : E  K9: 99: F L99::: 9=:                  %                                       ! 'A     "  
N 9 : E L 9: F  K9 9::L9L: 9:  9):    % 1')2                .          0!7                                    9  %                 E           
 9: FL :9:          -&440                    ! ''    %    ;M     1'C ',2 -      'AJ            %           "                            1'C'*2 ?                     ;M                              "      438&  &7         1'(2                          "    

 


 



 











30

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo               Ì  ! " #    ! #                    I             $        16C2 .     !  'A/ 3                      %         9            :                      
9    :           "           "    %             D                         "      % 1'(2                    16A6'2                         16A2                       ! !                      "        16'2  $                       % 16,2      "                 "                    %                                        !  ''/                  %    4..#&            
%                     9      !  '6      :     "                                              9   :       C           "       1'(2       C                .  E  E A


31





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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo                 5       708
quantum tunnelingd          
a
+a
+a
+a
  $       singletexciton                   1662  !  '6/ .                 1+C6+2 708   $                        "               16A2                              7                                  
                          7               
  $  E                        @    
                            @                 E  E A    $        %                                       %                                 $     7                       .                          0                    . ! '6  - 5           9  :         "      
                      C                 @   %                      "       0!7          $    .       $                    1',')2                                          "                              438& 4.#&      $              9O3.:    &7
%                  .                                                                            % Æ         Æ        .        
quasiparticle
c

1



2

3

4

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32

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96A'': 6'  

33

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo1')2 7 . / P 3  &  P  96A6':
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6A,',6

34

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Molecular Dynamics Study of Ultra-Long Chain
Fatty Acid in Lipid Bilayer
Kazutomo KAWAGUCHI1 , and Hiroshi NOGUCHI2
1
Institute of Science and Engineering, Kanazawa University
Kanazawa, Ishikawa 920-1192
2
Institute for Solid State Physics, University of Tokyo
Kashiwa-no-ha, Kashiwa, Chiba 277-8581

Abstract
Although ultra-long-chain fatty acids (ULCFAs) exist in biomembranes in certain typesof tissues, their biological roles remain unknown. In this report, we review our recent
study on the molecular conformation of ULCFAs in lipid bilayers using molecular dynamics simulations. Their long chains exhibit characteristic fluctuations between elongated, Lshaped, and turned conformations. The ratiosof these conformations are changed in responseto the lipid-density differences between upperand lower leaflets. This response is faster than
the flip–flop of cholesterols.

1

Introduction

Biological membranes consist of various typesof lipids and proteins [1]. Phospholipids are
the most abundant lipids in biological membranes and have a polar head group and twohydrocarbon tails (fatty acids). Phospholipids
are biosynthesized by a combination of a polar head group and two hydrocarbon tails andhave a range of structural and functional roles
in biological cells [2, 3]. Each tail typically contains between 14 and 22 carbon atoms. Fatty
acids containing more than 22 carbons arecalled very-long-chain fatty acids (VLCFAs).
Moreover, much longer chains containing morethan 32 carbons were found at the sn-1 posi-tion of phosphatidylcholine (PC) in photoreceptors, fibroblasts, and keratinocytes [4, 5, 6].
These fatty acids are called ultra-long-chain
fatty acids (ULCFAs). It is considered that
ULCFAs are stored as a precursor of bioactive
lipid mediators [7] and derivatives of C32:6 andC34:6 are neuroprotective in retina [8]. However, the physicochemical properties of ULCFAs with respect to their structures and thebiological roles of ULCFA-containing phospholipids are still unclear.
Molecular simulations have been widely applied to study lipid membranes [9, 10, 11, 12,13]. All-atom molecular dynamics (MD) simulations for VLCFAs, in which the tail length of
the sn-1 chain is maximally 24, have also been
performed by a few groups [14, 15, 16, 17, 18].
It has been shown that the long hydrocarbon
chain is slightly interdigitated into the opposite
leaflet [14, 16, 17]. However, ULCFAs had notbeen simulated yet. To examine the effects ofultra-long chains, we have conducted all-atom
MD simulations for ULCFAs [19, 20]. Our
study revealed that the longer chains of ULCFAs have more strongly interacted with the
opposite leaflet than those of VLCFAs. The
ultra-long sn-1 chain of ULCFAs takes elongated, L-shaped, and turned conformations inlipid bilayers and that these conformationalchange can reduce the lipid-density difference
between upper and lower leaflets.
Three types of ULCFAs are used in this35

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

study. The chain length and number of unsaturated bonds are varied. Lipid molecules
composing Host bilayer membranes consist of
single or two types of phospholipids. Moreover,cholesterols (CHOLs) are embedded in the bilayer membrane to examine the effect of the
flip–flop of cholesterols.

2

Methods

2.1

ULCFAsThe molecular structures of ULCFAs used inthis study are shown in Fig. 1. Dotriacontahexaenoic acid containing phosphatidylcholine(dTSPC, C32:6-C18:0), which has an ultralong chain of 32 carbons with six double bonds
at the sn-1 position [Fig. 1(a)]. Hexacosatetraenoic acid containing phosphatidylcholine(HSPC, C26:4-C18:0), which has a very-longchain of 26 carbons with four double bonds atthe sn-1 position [Fig. 1(b)]. Lacceroic acidcontaining phosphatidylcholine (LSPC, C32:0C18:0), which has an ultra-long chain of 32
carbons with no double bonds at the sn-1 position [Fig. 1(c)]. All of the ULCFAs havea stearoyl chain at the sn-2 position. HSPC
is constructed by truncating the long hydrocarbon chain of dTSPC at C26, and LSPC isconstructed by the saturation of the long hydrocarbon chain of dTSPC. A single ULCFA
molecule (dTSPC, HSPC, or LSPC) was inserted into the lipid bilayers described in thenext section.

2.2

Lipid Bilayer

The molecular structures of lipids composing bilayer membranes are shown in Fig.
2. Distearoyl phosphatidylcholine (DSPC,
C18:0-C18:0) contains two saturated stearoyl
chains.
Stearoyl-DHA phosphatidylcholine(SDPC, C18:0-C22:6) contains a steroyl chain
and a docosahexaenoyl chain with six double
bonds at the sn-1 and sn-2 positions, respectively.
Stearoyl-oleoyl phosphatidylcholine

Figure 1: Molecular structures of ULCFAs.
(a) dTSPC (C32:6-C18:0), (b) HSPC (C26:4C18:0), and (c) LSPC (C32:0-C18:0).

(SOPC, C18:0-C18:1) contains a steroyl chain
and an oleoyl chain with a double bond at thesn-1 and sn-2 positions, respectively.

Figure 2: Molecular structures of lipids composing bilayer membranes. (a) DSPC (C18:0C18:0), (b) SDPC (C18:0-C22:6), and (c)SOPC (C18:0-C18:1).
We considered three types of host lipid
bilayers for embedding a single ULCFA
molecule: (1) single-component bilayers. (2)asymmetric-component bilayers, in which eachleaflet is composed of a single type of phospholipids. (3) two-component bilayers composedof a single type of phospholipids and cholesterols.

36

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo(1) DSPC, SDPC, and SOPC were used
for single-component host bilayers and dTSPC
was used as ULCFA. A membrane bilayer consisting of 100 lipid molecules per leaflet was
prepared for each membrane system. The
membrane was connected by its periodic images in the xy plane under the periodic boundary conditions. A single lipid molecule in theupper leaflet was replaced by a single dTSPC
molecule for each single-component bilayer, inorder to examine the influence of the difference
between upper and lower leaflets on the longsn-1 chain of dTSPC (see Table 1). Ten lipid
molecules in the upper (lower) leaflet were removed in the ’189u’ (’189l’) system. Four lipid
molecules in the upper (lower) leaflet were removed in the ’195u’ (’195l’) system. Two lipid
molecules in the upper (lower) leaflet were removed in the ’197u’ (’197l’) system. Labels
189, 195, and 197 represent the total numberof the host lipid molecules. The number ofwater molecules per lipid was fixed at 50 in allcases. The lipids do not flip–flop to the opposite leaflets on a simulation time scale (the flip–
flop time is typically hours or days [21]). The
difference of the lipid numbers between the twoleaflets results in the deviation of the lipid density from a stable value even under zero surface
tension, as described by an area-differenceelasticity model [22, 23]. Thus, the lipids inthe leaflet with higher density are more compressed, although a flat membrane connectedby the periodic boundary does not bend because of the symmetry. The DSPC/dTSPC
and SDPC/dTSPC mixtures were equilibrated
for 200 ns, which was followed by 800-ns production runs at 343 K. to avoid the gel phaseof DSPC and SDPC. The SOPC/dTSPC mixtures were equilibrated for 400 ns, which was
followed by 1.6 µs-production runs at 303 K.
(2) A DSPC/SDPC mixture asymmetric bilayer was used as a host lipid bilayer. We
prepared six types of host lipid bilayers withdifferent numbers of lipids, as shown in Table 2. A single ULCFA molecule was inserted

Table 1: Numbers of phospholipids in singlecomponent host bilayers. A single dTSPC
molecule was embedded.
Model
Upper leaflet Lower leaflet
DS189u89 DSPCs
100 DSPCs
DS189l99 DSPCs
90 DSPCs
DS197u97 DSPCs
100 DSPCs
DS197l99 DSPCs
98 DSPCs
SD189u89 SDPCs
100 SDPCs
SD189l
99 SDPCs
90 SDPCs
SD197u97 SDPCs
100 SDPCs
SD197l
99 SDPCs
98 SDPCs
SO189u89 SOPCs
100 SOPCs
SO189l99 SOPCs
90 SOPCs
SO195u95 SOPCs
100 SOPCs
SO195l99 SOPCs
96 SOPCs
SO197u97 SOPCs
100 SOPCs
SO197l99 SOPCs
98 SOPCs

Table 2:
Numbers of phospholipids inasymmetric-component host bilayer membranes. Three types of ULCFAs were embedded in the bilayers.
MembraneUpper leaflet Lower leaflet
DS89-SD100 89 DSPCs
100 SDPCs
DS99-SD90
99 DSPCs
90 SDPCs
DS99-SD98
99 DSPCs
98 SDPCs
SD89-DS100 89 SDPCs
100 DSPCs
SD99-DS90
99 SDPCs
90 DSPCs
SD99-DS98
99 SDPCs
98 DSPCs

into the upper leaflet. The system temperature was controlled at 343 K to avoid the gelphase of DSPC and SDPC. The membranes
were equilibrated for 200 ns, followed by 800
ns production runs.
(3) We added cholesterols to both the upperand lower leaflets of the bilayer to investigate
the effects of cholesterols on the conformationof the ULCFAs. DSPC and SDPC were usedas the host lipid molecules. The number ofhost lipid molecules is 89 in the upper leaflet,and 100 in the lower leaflet. In the initial state,
20 or 40 cholesterols are added in both upper37

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Table 3: Numbers of lipids in bilayers containing cholesterols. A single dTSPC molecule wasembedded.
MembraneUpper leaflet Lower leaflet
DS/CHOL20 89 + 20
100 + 20
DS/CHOL40 89 + 40
100 + 40
SD/CHOL20 89 + 20
100 + 20
SD/CHOL40 89 + 40
100 + 40
and lower leaflets, as listed in Table 3. The
system temperature was controlled at 343 K
to avoid the gel phase of DSPC and SDPC.
The DS/CHOL and SD/CHOL mixtures were
equilibrated for 1 µs and 3 µs, respectively.

2.3

MD Simulations

The system pressure of all simulations was controlled at 0.101 MPa. The CHARMM 36 force
field [24] and TIP3P water model [25] wereused to represent lipid and water molecules, respectively. All MD simulations were performed
using GROMACS [26].
To calculate the lipid area in the tensionless membranes, we performed MD simulations
for a pure symmetric membrane consisting of
100 DSPCs or 100 SDPCs per leaflet. Fromthese MD simulations, we obtained that thearea per lipid of DSPC and SDPC are 0.63
and 0.74 nm2 , respectively. Thus, SDPCs havea slightly larger area than DSPCs because of
their unsaturated bonds.

3

Results and Discussion

3.1

Conformation of the sn-1 chainof ULCFAs

Figure 3 shows three types of conformations of
the sn-1 chain of dTSPC embedded in SD189u
bilayer. A turned conformation, in which the
C3 -C18 -C32 angle in the sn-1 chain is approximately 0◦ is shown in Fig. 3(a). The terminal carbon atom (C32 ) is located in the upper
leaflet. An L-shaped conformation, in which
the angle is approximately 90◦ , is shown in Fig.

3(b). The C32 atom is located at the boundaryof the two leaflets. An elongated conformation,in which the angle is approximately 180◦ , isshown in Fig. 3(c). The C32 is located in thelower leaflet. The sn-1 chain temporally fluctuates among these conformations. That is,
it moves between the upper and lower leaflets
and also lies along with the interface between
the two leaflets. This conformational fluctuation is rapid, and the transit time between two
leaflets is ∼ 10 ns. This large conformational
fluctuation has not been observed in the otherlipids and is specific to ULCFAs. In particular,
the elongated conformation penetrates deeply
into the opposite leaflet.
These three types of conformations were also
observed for dTSPC, HSPC, and LSPC embedded in asymmetric membranes (case 2) under all simulated conditions. Three types ofconformations of dTSPC, HSPC, and LSPC
are shown in Fig. 4(a), (b), and (c), respectively. The long chain of LSPC is more elongated than that of dTSPC owing to the saturated bonds of the chain.

3.2

Distribution of atoms

The distribution of atoms in the sn-1 chain of
ULCFA in a lipid bilayer was calculated along
the z-axis, which is parallel to the bilayer normal. The origin of the z-axis is set at the centerof the lipid bilayer. Figure 5 shows the probability distribution of C3 , C18 , and C32 atomsof the sn-1 chain of dTSPC and phosphate (P)atoms of DSPC, SDPC, and SOPC in singlecomponent bilayers (case 1). The terminal carbon C32 of the sn-1 chain is widely distributed
from z of −2 to 2 nm, corresponding withthe conformation fluctuations observed in thesnapshots. The free-energy difference F (z) between two conformations were calculated using
F (z) = −kB T ln(p(z)). The estimated value
was less than 1.2kB T from p(z) of C32 , indicating no apparent free-energy barrier amongthem.
The distributions of P in DS189u, SD189u,38

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Figure 3: Snapshots of dTSPC in DS189l
membrane. (a) Turned conformation. (b) Lshaped conformation. (c) Elongated conformation. dTSPC are represented by coloredspheres. Host lipid molecules are shown in gray(spheres represent the phosphate atoms). Water molecules are not shown for clarity.

and SO198u were similar to those in DS189l,
SD189l, and SO198l, respectively. as shown in
Fig. 5. These results indicate that the membrane thickness does not depend on the lipiddensity difference between the two leaflets.
Thus, the host lipid bilayer structure is not
significantly modified in the examined rangeof the density difference.
In contrast, the distribution of carbon atoms
in the sn-1 chain of dTSPC depends on
the lipid-density difference between the twoleaflets. As the lipid density relatively de-

Figure 4: Snapshots of (a) dTSPC, (b) HSPC,
and (c) LSPC embedded in lipid bilayers(DS89-SD100). Carbon atoms in the sn-1
chain are shown in cyan and other carbon
atoms are shown in gray. Hydrogen atoms
are not shown for clarity. Reproduced from
Ref. [20].

creases in the lower leaflet, the distributionof C32 largely shifts toward the lower leaflet.
The peak position of C18 shifts toward thelower leaflet as observed in C32 , but the shiftmagnitude is smaller. The distribution of C3
shifts slightly toward the lower leaflet as thelipid density relatively decreases in the lower
leaflet. Thus, the longer region (C18 ∼ C32 ) of
the sn-1 chain exhibits larger changes arisingfrom the lipid-density difference between the
two leaflets.
To quantitatively investigate the effects of
the lipid-density differences on the conformation of the sn-1 chain, we calculated the mean
z-position of the C atoms normalized by themean z-position the P atoms as a function of
the lipid density ratio. The lipid density ratio

39

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo
(a)
0.20

dTSPC-sn1-C3
dTSPC-sn1-C18
dTSPC-sn1-C32
DSPC-P

p(z)

0.15
0.10

0.05

0.00
(b)
0.20

-3

-2

-1

0
z / nm

1

2

3

-1

0
z / nm

1

2

3

-1

0
z / nm1

2

3

dTSPC-sn1-C3
dTSPC-sn1-C18
dTSPC-sn1-C32
SDPC-P

p(z)

0.15
0.10

0.05

0.00
(c)
0.20

-3

-2

dTSPC-sn1-C3
dTSPC-sn1-C18
dTSPC-sn1-C32
SOPC-P

p(z)

0.15
0.10

0.05

0.00

-3

-2

the normalized z-positions of carbon atoms
and the lipid density ratio.
From the least-squares fitting (represented
by dashed lines), we found that the value ofzC /zP was almost 0 for C32 at Nl Sl /Nu Su = 1.
Thus, the terminal carbon is located in the
middle of the lipid bilayer in the absence oflipid-density difference and moves to the upper (lower) leaflet as the lipid density of theupper (lower) leaflet decreases. Therefore, theconformational change of the sn-1 chain in dTSPC reduces the lipid-density differences.
In the cases of HSPC and LSPC, we foundsimilar behavior. The ratio of the elongatedconformation increases as the lipid density ofthe opposite leaflet decreases. The position theterminal atom C26 of HSPC is almost identical to that of C26 in dTSPC. The sn-1 chain of
LSPC is more deeply in the lower leaflet thanthat of dTSPC. We concluded that the reduction of the lipid-density differences between the
two leaflets by the change in the ultra-long sn1 chain conformation is a general property of
ULCFAs.

3.3
Figure 5: Probability distribution of atoms in(a) DS189u/l, (b) SD189u/l, and (c) SO189u/l
systems. The solid and dotted lines represent
the probability distributions in ’u’ and ’l’, respectively. Reproduced from Ref. [19].

is defined as Nl Sl /Nu Su , where Nl and Nu are
the numbers of host lipid molecules in the lower
and upper leaflets, respectively. Sl and Su represent the area per lipid of DSPC or SDPCin the lower and upper leaflets, respectively,
obtained from the MD simulations for a puresymmetric membrane.
Figure 6 shows the correlation between thenormalized z-position (zC /zP ) and the lipid
density ratio Nl Sl /Nu Su for dTSPC in the
DSPC/SDPC asymmetric bilayer (case 2). A
positive linear correlation was found between

Order parameters

We calculated the lipid order parameters, SCD ,using the following equation:
SCD = ⟨

3 cos2 α − 1
⟩,
2

(1)

where α is the angle between C-H bond vector and the bilayer normal. The brackets indicate the average over time and lipid molecules.
Calculated order parameter profiles, −SCD , for
ULCFAs embedded in the DS89-SD100 bilayer
are shown in Fig. 7. These profiles were notsensitive to lipid-density differences. The order profiles of the sn-1 chain decreased to 0
at C15 and exhibited a low order in the longer
region until the terminal. The order of the sn2 chain was similar to that of the sn-2 chain
in DSPC, because the sn-2 chain in ULCFAs
are equivalent to the sn-2 chain in DSPC. In
the case of HSPC, the order profile of the sn-1

40

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo(a) DSPC (upper) / SDPC (lower)
DS99-SD90
DS99-SD98
0.3

(a)
0.3

DS89-SD100

0.2

0.2
-SCD

zC / zP

0.1
0.0
-0.1

C18
C26
C32
y = 0.44 x -0.34
y = 0.56 x -0.53
y = 0.54 x -0.53

-0.2
-0.3
1.0

dTSPC sn-1
dTSPC sn-2

1.1

1.2
NlSl / NuSu

1.3

0.1

0.0
0

5

10

(b)

1.4

15 20
Carbon0.3(b) SDPC (upper) / DSPC (lower)
SD99-DS90 SD99-DS98
SD89-DS100
0.3

25

30

35

30

35

30

35

HSPC sn-1
HSPC sn-2

-SCD

0.2

0.2

0.1

zC / zP

0.1
0.0

0.0

0

-0.1

C18
C26
C32
y = 0.52 x -0.39
y = 0.83 x -0.78
y = 0.86 x -0.85

-0.2
0.8

0.9
NlSl / NuSu

1.0

1.1

15 20
Carbonchain exhibited a similar profile to that in dTSPC, because the sn-1 chain in HSPC is equivalent to the sn-1 chain until C26 in dTSPC. In
contrast, the order profile of LSPC becomes
higher toward the terminal, because the sn-1
chain of the LSPC has no double bonds. It is
indicated that the saturated long chain takes
a more elongated conformation than unsaturated long chains.

dTSPC with Cholesterols

We analyzed the interactions between dTSPC and cholesterols in asymmetric bilayers.
Cholesterol molecules were embedded in theasymmetric bilayers with number fractions of
20 % or 40 %, as shown in Table 3. In theinitial state of each simulation, the cholesterol

25

LSPC sn-1
LSPC sn-2

0.2

Figure 6: Correlation between normalized zposition of C atoms and lipid-density ratio for
dTSPC. Reproduced from Ref. [20].

3.4

10

0.3

-SCD

-0.3
0.7

5

(c)

0.1

0.0
0

5

10

15 20
Carbon25

Figure 7: Order parameter profiles, −SCD ,of (a) dTSPC, (b) HSPC, and (c) LSPC for
DS89-SD100. Reproduced from Ref. [20].

fraction in the upper leaflet (Nupper /Ntotal ) was
set to 0.5.
In the case of the DSPC bilayers, the cholesterol fraction is almost constant (0.5) during 1µs, showing that the flip–flop time of cholesterols is much longer than 1 µs. On the other
hand, in the case of the SDPC bilayers, thefraction frequently changed during 3 µs, showing that the lipid-density difference between
the two leaflets was reduced by cholesterols.
The flip–flop times per cholesterol molecule
were 0.52 and 0.66 µs for SD/CHOL20 and41

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

SD/CHOL40, respectively. These values offlip–flop time are much faster than those ofphospholipids (hours or days) but slower thanthose of ULCFAs. A similar time scale of thecholesterol flip–flop was previously reported in
the MD simulations of other lipid compositions[27, 28, 29]. After the cholesterol flip–flop, theconformation distribution of ULCFAs becomesclose to that in the absence of the area difference in the two leaflets. Therefore, the area
difference is responded by ULCFAs first andlater by cholesterols. ULCFAs is a rapid sensor of the area difference.

4

Summary

In this report, we have shown the conformational change of ULCFAs in single-component
and asymmetric bilayer membranes using MD
simulations. The ultra-long sn-1 chain largely
fluctuates between upper and lower leaflets andtakes elongated, L-shaped, and turned conformations. ULCFAs can rapidly reduce the lipiddensity differences between the two leaflets via
the ratio change of these conformations.

References[1] G. van Meer, D. R. Voelker, and G. W.
Feigenson, Nature Rev. Mol. Cell Biol. 9,112 (2008).
[2] H. Shindou and T. Shimizu, J. Biol.
Chem. 287, 1 (2009).
[3] B. Anthony, S. Vanni, H. Shindou, and T.
Ferreira, Trends Cell Biol. 25, 427 (2015).

[7] B. Jun, P. K. Mukherjee, A. Asatryan, M.
A. Kautzmann, J. Heap, W. C. Gordon,
S. Bhattacharjee, R. Yang, N. A. Petasis,
N. G. Bazan, Sci. Rep. 7, 5279 (2017).
[8] F. Deák, R. E. Anderson, J. L. Fessler, D.
M. Sherry, Front Cell Neurosci. 13, 428(2019).
[9] M. Müller, K. Katsov, and M. Schick,Phys. Rep. 434, 113 (2006).
[10] M. Venturoli, M. M. Sperotto, M. Kranenburg, and B. Smit, Phys. Rep. 437, 1(2006).
[11] H. Noguchi, J. Phys. Soc. Jpn. 78, 041007(2009).
[12] R. M. Venable, F. L. H. Brown, and R.
W.Pastor, Chem. Phys. Lipids 192, 60(2015).
[13] S. J. Marrink, V. Corradi, P. C. T. Souza,
H. I. Ingólfsson, D. P. Tieleman and
M. S.P. Sansom, Chem. Rev. 119, 6184
(2019).
[14] A. P. Ramos, P. Lagë, G. Lamoureux, andMichel Lafleur, J. Phys. Chem. B 120,
6951 (2016).
[15] R. Gupta, B. S. Dwadasi, and B. Rai, J.
Phys. Chem. B 120, 12536 (2016).
[16] T. Róg, A. Orlowski, A. Llorente, T.
Skotland, T. Sylvänne, D. Kauhanen, K.
Ekroos, K. Sandvig, and I. Vattulainen,
Biochim. Biophys. Acta 1858, 281 (2016).

[4] J. P. SanGiovanni and E. Y. Chew, Prog.
Retin. Eye Res. 24, 87 (2005).

[17] M. Manna, M. Javanainen, H. M. Monne,
H. Gabius, T. Rog, and I. Vattulainen,
Biochim. Biophys. Acta 1859, 870 (2017).

[5] A. McMahon and W. Kedzierski, Br. J.
Ophthalmol. 1127 (2009).

[18] E. Wang and J. B. Klauda, J. Phys.
Chem. B 122, 2757 (2018).

[6] P. Barabas, A. Liu, W. Xing, C. Chen, Z.
Tong, C. B. Watt, B. W. Jones, P. Bernstein, and D. Križaj, Proc. Natl. Acad.
Sci. USA 110, 5181 (2013).

[19] K. Kawaguchi, K. M. Nakagawa, S. Nakagawa, H. Shindou, H. Nagao, and H.
Noguchi, J. Chem. Phys. 153, 165101(2020).

42

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo[20] K. Kawaguchi, H. Nagao, H. Shindou, and
H. Noguchi, J. Phys. Chem. B 126, 9316
(2022).
[21] R. D. Kornberg and H. M. McConnell,
Biochemistry 10, 1111 (1971).
[22] U. Seifert, Adv. Phys. 46, 13 (1997).
[23] S. Svetina and B. Žekš, Adv. Colloid Interface Sci. 208, 189 (2014).
[24] J. B. Klauda, I. M. Venable, J. A. Freites, J. W. O’Connor, D. J. Tobias, C.
Mondragon-Ramirez, I. Vorobyov, A. D.
MacKerell Jr., and R. W. Pastor, J. Phys.
Chem. B 114, 7830 (2010).
[25] A. D. MacKerell Jr. and P. W. Pastor, J.
Phys. Chem. B 102, 3586 (2010).
[26] M. J. Abraham, T. Murtola, R. Schulz, S.
Páll, J. C. Smith, B. Hess, and E. Lindahl,
SoftwareX 1, 19 (2015).
[27] S. Jo, H. Rui, J. B. Lim, J. B. Klauda,
and W. Im, J. Phys. Chem. B 114, 13342(2010).
[28] F. Ogushi, R. Ishitsuka, T. Kobayashi,
and Y. Sugita, Chem. Phys. Lett. 522, 96(2012).
[29] R. Gu, S. Baoukina, and D. P. Tieleman, J. Chem. Theory Comput. 15, 2064
(2019).

43

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

3.2

First-Principles Calculationof Material Properties

44

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Theoretical study on dynamical processes inheterogeneous catalysis using density functional
theory and machine learning methods
H. H. HALIM, T. N. PHAM,Y.-L. WANG, T. OTA, S. E. M. PUTRA
Y. HAMAMOTO, K. INAGAKI, I. HAMADA, and Y. MORIKAWA
Department of Precision Engineering, Graduate School of Engineering,Osaka University, 2-1 Yamadaoka Suita, Osaka 565-0871

In 2022-2023, we carried out theoretical investigation of chemical processes at surfaces
and interfaces, Cu-Zn surface alloy formation on Cu(997), Hydrogenation of formate
species using atomic hydrogen on a Cu(111)model catalyst, oxygen vacancy migrationin SrFeO3−δ and Sr3 Fe2 O7−δ , NO-H2O coadsorption on Cu(111), and activity and selectivity of N2 ﬁxation on B doped g-C9 N10 .
In this report, we report Cu-Zn surface alloy formation on Cu(997)[1]. The catalysts
based on metal-alloys are well recognized in
the ﬁeld of heterogenous catalysis considering
the emerging of unique properties upon alloying. Given the optimal composition and environment, these catalysts can oﬀer high catalytic performance that outperform their alloy
constituents. There have been notorious examples on the application of metal-alloy based
catalysts in heterogenous catalysis. In thepresent study, we focus on Cu-Zn surface alloy
which has been reported to be very active formethanol synthesis by hydrogenation of CO2 .
Experimental studies have shown that the deposition of Zn on Cu(111) promotes the activity of methanol production by an order ofmagnitude at Zn coverage of 0.197. In addition, the Cu-Zn alloy has been reported as agood model of Cu/ZnO/Al2 O3 (CZA), the industrial catalyst used to synthesize methanol
by hydrogenation of CO2 . Thus, this alloy has
been extensively involved in the investigationof the true active site of CZA which remains
under controversy.
Recently, it was reported that by coupling
Molecular Dynamics (MD), active-learning,
and automated event detection, Lim et al.
have successfully reveal the restructuring
mechanisms of Pd deposited on Ag. By adopting similar methodologies, we performed MD
simulation of Zn depositions on Cu(997) surface to explicitly capture the mechanisms thatare responsible to the Cu-Zn surface alloying.
The MD simulations were implemented basedon force-ﬁeld that was trained by means ofGaussian Process (GP). The database, containing information of atomic forces acting
on various environments, were obtained from
DFT calculations. The task of training wasdone by using Fast Learning of Atomistic Rare
Events (FLARE) code which utilizes the uncertainty information from GP, together withactive on-the-ﬂy learning scheme to build theforce-ﬁeld eﬃciently.
We used DFT calculations to provide forcesof the central atom on each atomic environment. All DFT calculations were performed
using the STATE code. The electron-ion
interaction was described by ultrasoft pseudopotentials. We took into account the vander Waals (vdW) interactions by applying
optB86b-vdW exchange correlation functional
as implemented in the STATE code.
To construct GP force-ﬁeld, we used FLARE

45

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

code with two- and three-body multispecies
kernels. The interaction cutoﬀs for two- andthree-body kernels are set to 7.0 Å and 4.5 Å,respectively. The hyperparameters embedded
to these kernels, namely two-body signal variances (σ2 ), two-body length scale (l2 ), threebody signal variances (σ3 ), three-body lengthscale (l3 ), and noise hyperparameter (σn ) was
optimized by maximizing the log marginal likelihood using BFGS algorithm as implemented
in SciPy used inside the FLARE code. The
database is constructed by employing active
and on-the-ﬂy learning scheme that update thedatabase based on uncertainty information calculated by GP. In order to provide new atomic
environments to be candidate for the database,
MD code internal to FLARE is used to evolve
atomic structures with 1 fs timestep within
canonical (NVT) ensemble. The temperature
is set up at 700 K with all atoms are allowed to
move. To obtain more diverse atomic environments, we manually changed the initial structure after certain MD steps during the on-theﬂy learning when no signiﬁcance improvementin the Mean Absolute Error (MAE) was found.
The details of this procedure and the resulting
database are reported in the next section.
To observe the alloying process of Cu-Zn,
we performed MD simulations within NVT ensemble at temperature 700 K for up to 6.25
µs. Figure 1 shows the evolution of deposited
Zn atoms on Cu(997) until the Cu-Zn surface
alloy is formed. To clearly observed the number of Zn atoms that substituted at the terrace, we also plotted the composition of Cu
and Zn atoms as a function of rows for every
certain timesteps. Such plots accompany each
MD snapshots in Figure 1. The snapshots of
the MD simulation are also presented as video
in the Supporting Information. In the beginning (Figure 6a), the Zn atoms are found to
localize at the step edge, covering the original Cu step adatoms. The proﬁle of boundary
between Cu and Zn is still fairly linear. After
1.25 µs, some of the Zn atoms are incorporated

Figure 1: The snapshots of MD simulations
showing the evolution of deposited Zn atoms
on Cu(997). The green to yellow atom indicates the Cu atom, the red atom indicates
Zn as adatom, and blue atom indicates substituted Zn atom. The two plots accompanied
each snapshot show the composition of atom as
a function of row. In each plot, the red, blue,
light green, and dark green bars indicate thenumber of Zn as adatom, Zn as incorporated
atom, Cu as surface atom, and Cu atom whichis covered by additional Cu or Zn layer.
at the upper and lower terrace near the stepedge (Figure 1b). At this stage, almost no Zn
atoms are incorporated at the middle terrace.
We also noticed Zn atoms are dominantly incorporated to the upper terrace whereas only
few incorporated Zn atoms found at the lower
terrace. After 2.5 µs (Figure 1c), the distribution of the Zn on the surface and the number ofincorporated Zn atom are still relatively similar.

References[1] Harry Handoko Halim and Yoshitada
Morikawa: ACS Physical Chemistry Au
2, 430-447 (2022).

46

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Prediction of Material Properties using Density Functional
TheoryOsamu Sugino
Institute for Solid State Physics,
The University of Tokyo, Kashiwa-no-ha, Kashiwa, Chiba 277-8581
We have studied properties of cuprate

The accuracy of the xc functionals issuperconductors and zirconia cathode materials

essential in providing reliable prediction ofusing the SCAN and PBE exchange-correlationmaterial properties. We tried to improve the(xc) functionals, respectively, of the density

accuracy using a machine learning method. We

functional theory (DFT). Our DFT calculations

used the accurate electronic structures of smallof cuprates reproduced lattice properties andmolecules provided by a quantum chemicalelectronic band structures consistent withcalculation together with the theoreticallyavailable experimental data and predicted

derived properties of the xc functionals [4].

properties of layered ones [1]. Our calculatedThose different data of molecules and theory

free energy landscape for the oxygen reduction

are found to play a complementary role inreaction occurring on the zirconia surface

increasing the accuracy and numerical stability

suggested that a high reactivity appears notof the functional as demonstrated by calculation

only when doped with nitrogen atoms andof various crystals.

oxygen vacancies also in the pristine conditions.
It is therefore possible that the role of defects

References

may be increase of the carrier conductivity[1] A. N. Tatan, et al., AIP Advances 12,

rather than enhancement of the reactivity [2].

105308 (2022).

We have used for the electronic structure[2] S. Muhammady, et al., J. Phys. Chem. C

calculations the packages VASP and Quantum

126, 15662 (2022).

Espresso

ISSP[3] https://www.pasums.issp.u-tokyo.ac.jp/abics

supercomputers. We have used for modeling[4] R. Nagai et al., Phys. Rev. Research 4,

the defective zirconia the abICS package

013106 (2022); Electron. Struct. 1, 37-38

developed by S. Kasamatsu [3].

(2022).

implemented

in

the

47

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Clarification of Microscopic Mechanisms of Semiconductor Epitaxial
Growth and Device-Interface Formation by Large-Scale Quantum-TheoryBased Computations
Atsushi Oshiyama
Institute of Materials and Systems for Sustainability, Nagoya University
Furo-cho, Chikusa-ku, Nagoya 464-8601
In the fiscal year of 2022, on the basis of theproperties. Several methods have been proposed

total-energy electronic-structure calculations andto produce graphene sheets. Among them, an

molecular

within theemerging technology consists in the thermal

density-functional theory (DFT), we have studied

decomposition of silicon carbide (SiC). The major

the epitaxial growth of wide-gap semiconductors

advantage of this technique is the possibility ofand graphene sheets, atomic and electronic struc-growing graphene directly on a semiconductingtures of semiconductor surfaces and their inter-

substrate, thus avoiding the difficult step offaces with insulators. The main computational

extracting and transferring graphene sheets on atools are our RSDFT (Real Space DFT) code andsubstrate. From a scientific standpoint, the forma-

RS-CPMD (Car-Parrinello Molecular Dynamics)

tion of graphene from a SiC crystal, possibly

dynamics

simulations

code as well as VASP code. Specifically, we havestudied 1) the atom-scale mechanism of GaN epitaxial growth [1,2], 2) the mechanism of graphene
growth on SiC surfaces [3], 3) atomic and electronic structures of stepped SiC surfaces [4] andGaN/AlSiO interfaces [5], and 5) the role of point
defects in SiN which have been speculated to be
the principal element of current flash memories.
The below is the explanation of the issues 2) and

Fig. 1: Free-energy profile of Si desorption
from the step edge and major relevant
configurations. The color code for the atoms isblue for Si and brown for C. The collective
variable (CV) used in the meta-dynamics isthe distance between a desorbing Si (redsphere) and the center of mass (black sphere)of the lower terrace site formed by three C
atoms.

5) above.

Initial stage of graphene formation on
SiC(0001) Surfaces
Graphene has a consolidated background as aninnovative

two-dimensional(2D)

material

because of the peculiar electronic and mechanical48

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyoof a graphene sheet. We have found that asubsequent Si desorption leaves behind undercoordinated C atoms and this, in turn, triggers theformation of three stable C-C bonds via an
exothermic reaction. This second step paves theroute to the formation of larger carbon structure at
the SiC(0001) surface. Following the guidelinesthat these simulations indicate, we focused on theprocesses occurring upon desorption of several Si
atoms. We have clarified how small C clusters

Fig. 2: Free-energy landscape (the bottom-left)
and minimum free-energy reaction pathway(path1 + path2 + path3: the dotted, solid anddashed lines) for the clustering of carbon
atoms. The labels from (I) to (VI) in thelandscape indicate the positions of the (meta)
stable configurations along the pathway. The
stable configurations (I) and (VI) are shown in
the top-left and the bottom-right, respectively.
In (I), C5 and C4 clusters are highlighted withmagenta and green spheres, respectively.
Those clusters become a single cluster C9 in
the final configuration (VI). The CV1 is the
distance between the COM of C5 (left black
sphere) and COM of C4 (right black sphere)
and the CV2 is the distance between the COM
of C5 and a C atom (yellow sphere).

formed at the exposed surface can merge into alarger structure presenting a C6 ring, the expected
seed of a graphene-like conformation (Fig. 2).
The extension of this carbon bond network
realized in this way is accompanied by aremarkable energy gain, leading to an exothermic
reaction and to a stable carbon structure precursorof an actual graphene flake.

Nitrogen Vacancy in Silicon Nitride:
Emergence of Floating Gap Statesaccompanied by a selective removal of Si atoms

Silicon nitride （SiN） thin films are fabricatedand subsequent condensation of C atoms, is stillnear interfaces of Si-MOS structures, and utilized

an unexplored issue.

as memory elements in non-volatile

flash

Based on the first principles molecular dyn-memories which sustain our energy-saving society.

amics empowered by free energy sampling

The microscopic mechanism of the memory

methods, we have clarified atomistic processes offunction

the initial stage of graphene formation on

performed

SiC(0001). Our simulations have shown that theapproximation

desorption of a Si atom from a step edge and its

functional and clarified the atomic and electronic

subsequent migration to a stable site on the

structures of nitrogen vacancy (VN) in SiN. The

nearby terrace is an endothermic reaction (Fig. 1).

results indicate that the floating electron state

Yet, this step is an essential initial trigger to theinherent to host SiN is localized near VN andprocess that eventually will lead to the formationinduce a gap state which is free from energy49

isunknown,DFT
tohowever.

calculations
the

We

with

have
hybridexchange-correlation

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

barriers upon electron capture and emission, thus
being a strong candidate for the memory unit.

Fig. 4: KS orbital of localized the floating statein the gap of SiN. Isovalue surface of theorbital from (a) a perpendicular and (b) aparallel directions to a plane consisting of 3neighbor Si atoms.

Fig. 3: KS orbital of the CBB of β-Si3N4
viewed from c axis (a) and from its
perpendicular direction (b). The isovalue
surface of the KS orbital is shown by theyellow (+) and bluegreen (-) blobs. The blueand gray balls depict Si and N atoms,
respectively.

the exchange splitting.
More interestingly, the floating state near CBB(Fig. 3) capture additional electrons and localizednear VN (Fig. 4). This is due to the ionicity of

Figure 3 shows the Kohn-Sham (KS) orbital of

SiN: The N atom in SiN is an anion so that VN isthe conduction-band bottom (CBB) of SiN (itscrystallineform

β-Si3N4).

The

orbitaleffectively positive, thus attracting electrons. Thisisgenerates additional gap states, making VN

distributed not near atomic sites but in the internal

multiply charged. The floating state localized inspace, showing the floating character. We havethe N-deficit region may be responsible for thefound from the DFT calculations that the floating

memory function.

state is ubiquitous in various sparse materials such

The group id codes for the achievements

as carbon nanomaterials and even in typicalabove are k0042 and m0037.

tetrahedral-symmetry semiconductors [Matsushita,
Oshiyama: PRL 2012, 2014, Nano Lett. 2017].

Related publications

When a single N atom is removed from SiN[1] M. Boero, K. M. Bui, K. Shiraishi, K. Ishisone,
Y. Kangawaa, and A. Oshiyama, Appl. Surf. Sci.
599, 153935 (2022).
[2] K. M. Bui, K. Shiraishi and A. Oshiyama,Appl. Surf. Sci. 613, 155840 (2023).
[3] M. Boero, F. Imoto and A. Oshiyama, Phys.
Rev. Materials 6, 093403 (2022).
[4] T. Kimura, K. Chokawa, K. Shiraishi, and A.
Oshiyama, Phys. Rev. B 106, 035309 (2022).
[5] K. Chokawa, K. Shiraishi and A. Oshiyama,(i.e., VN), the nearest neighbor three Si atoms lose
their partner, inducing 3 Si dangling bonds (DBs).
These DB states are linear combined and induce a
totally symmetric state in the valence band andremaining two states in the gap. Depending on theposition of the Fermi level in the gap, VN changes

J. Appl. Phys 133, 065301 (2023).
[6] F. Nanataki, K. Shiraishi, J.-I. Iwata, Y.-i.
Matsushita, and A. Oshiyama, Phys. Rev. B 106,155201 (2022)

its charge state from +1 to -3, showing interesting
competition between the Jahn-Teller splitting and50

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Understanding structure-property relationships of disorderedmaterials using first-principles based high-throughput
simulation framework
Shusuke KASAMATSU
Academic Assembly (Faculty of Science),
Yamagata University, Kojirakawa, Yamagata-shi, Yamagata 990-8560
Characterizing the order within disorder of

to learn energetics for varying compositions

atomic arrangements in materials is imperative

were discussed [2].

for predicting, understanding, and designing a

As a first application to an unsolved

wide variety of materials functionalities such as

materials science problem, abICS was used toelectronic,

thermalunderstand the hydration behavior of heavily

conductivity/insulation and catalytic activity.

Sc-doped BaZrO3, known as a promising

Recently, we have been focusing on developing

perovskite proton conductor for solid oxide fuelmethods

statisticalcells. Although this class of materials has beendisorderedknown as a good proton conductor for 40 years,

systems, and we have also started to consider

the local structure that activates the hydration

how to analyze such huge amounts of data toreaction, which is the key reaction to introduce

extract

protons into the system, remained unknown.

ionic,

toandperform

thermodynamics

sufficient

sampling

meaningfulinstructure-property

relationships.

Using abICS, we simulated the ratio of Sc-VO-

For sampling of disordered crystalline

Sc, Sc-VO-Zr, and Zr-VO-Zr that contribute tosystems, we had already introduced our abICS

hydration under experimental thermodynamic(ab Initio Configuration Sampling) framework

conditions. We also obtained a machine-for deriving an on-lattice neural network model

learning model for lattice volumes andfromfirst-principles

in

past

predicted

year,

we

lattice constants in excellent agreement withbenchmarked its performance on calculating

experiment. Careful comparison with in situ

temperature-dependent disorder in the cation

XAS and thermogravimetry measurements leadsublattice of three spinel oxides. The efficiency

to the conclusion that Sc-VO-Sc and Sc-VO-Zrof the approach in an active-learning setting

are the active sites for hydration [3].

Activity

Reports

calculations
[1].

This

was demonstrated clearly against random
sampling,andissuessuchasdoping and hydration-dependent

In the case of liquid and amorphous systems,

accuracy

we have been relying on available software fordegradation vs. lattice relaxation and the ability

neural network potential training such as

51

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Simple-NN [4]. Training a reliable model forcharacterize the subtle difference between

liquid and amorphous systems up to two

liquid and amorphous ZnCl2 in terms of thecomponents is now relatively easy to achieve,

intermediate-range order, which may be related

and for such systems, we have now started toto the exponential decay in viscosity at theconsider how to understand the order withinglass transition.

disorder. Towards this end, we have been
examining the applicability of topological data

References

analysis [5], which characterizes the structure[1] S. Kasamatsu, ISSP Supercomputer Centerin terms of the size and shapes of rings andActivity Report 2020, pp. 16-26.

cavities. Figure 1 shows the temperature-[2] S. Kasamatsu et al., J. Chem. Phys. 157,

dependent first order persistence diagram for104114 (2022).

liquid and amorphous ZnCl2, where each dot[3] K. Hoshino, S. Kasamatsu, et al., Chem.

corresponds to a ring of Zn atoms; the “birth”

Mater. 35, 2289-2301 (2023).

axis corresponds to the bond distance for[4] K. Lee et al., Comp. Phys. Comm. 242, 95

forming the rings, and the “death axis” roughly(2019).

corresponds to the roundness of the rings. A[5] I. Obayashi et al., J. Phys. Soc. Jpn., 91,

clear change is seen in the ring distribution vs.

091013 (2022).

temperature,suggesting

that

we

can

Fig. 1: First order persistence diagrams of liquid and amorphous ZnCl2.

52

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Development of first-principles calculation code
RSPACE and design of highly functional interface
Tomoya ONO
Graduate School of Engineering, Kobe University
Rokkodai-cho, Nada, Kobe 657-8501

surface. We considered the modification incorporating four Si vacancies (VSi s) and 16 N
atoms at C sites (NC s). The cases in which
VSi s were arranged parallel to the surface were
investigated to evaluate the anisotropy of theformation energy of the nitride layer. In ourmodel, the areal N-atom density was on theorder of 1014 cm−1 , which is consistent withthe experimental results. Although there are
two inequivalent lattice sites of 4H-SiC, i.e., h(hexagonal) and k (quasi-cubic) sites, our previous studies have shown that nitride layers
tend to grow at the k site. Thus, we consider
the cases in which VSi s are arranged at the ksite. We also investigated the interfaces with
the nitride layer at the second layer for all thefaces. For the first-principles calculation, weused the RSPACE code[1, 2], which is based on
the real-space finite-diﬀerence approach within
the frameworks of density functional theory(DFT) and is developed in this project. We
employed the local density approximation of
the DFT to describe the exchange and correlation eﬀects. Electron-ion interactions were approximated by the projector-augmented wave
method. The structural optimization was performed until the residual forces are smaller
than 0.001 Hartree/Bohr radius.

4H-silicon carbide (4H-SiC) is an IV-IV
type semiconductor with a wide band gap of
3.2 eV, and the 4H-SiC based metal-oxidesemiconductor field-eﬀect transistor (MOSFET) is expected to be used in next generation switching devices operating at high powerand high frequency applications. However, thepotential of 4H-SiC has not been fully utilized owing to the low on current of MOSFETs, which is caused by defects at the 4HSiC/SiO2 interface. The post-oxidation annealing process using N2 O, NO, or N2 gas has
been proposed to reduce the density of interface defects and increase the on current. In
our previous study, we investigated the totalenergies and electronic structures of the nitride layers by density functional theory (DFT)
calculation using the surface models, where
the 4H-SiC/SiO2 interfaces are modeled by the
OH terminated surfaces. However, the surface
models do not include the eﬀect of the SiO2
networks on the interface atomic structure. In
this project of 2022, we employ the models containing the SiO2 layers on the SiC substrate.
Figure 1 shows our computational models,
where the surface of the 4H-SiC substrate is
terminated with the SiO2 layer and the otherside of the substrate is terminated with H
atoms. The rectangular supercells of 10.1 Å ×
5.3 Å × 26.3 Å for the a-face model, 10.1
Å × 6.2 Å × 27.3 Å for the m-face model,
and 18.5 Å × 5.3 Å × 26.8 Å for the Siface model were employed. The z-axis was
taken to be the direction perpendicular to the

The formation energy of the above interaction for generating one VSi to insert a nitride
layer was obtained as(w/ mod)

Eform = Etotal/4 + E(SiO2 )(w/o mod)+4µCO + 2µN − Etotal

53/4 − 6µNO , (1)Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo(a)a face (1st)m face (1st)C face (1st)PN (atm)
10-3
10-4
2

Eform (eV)

10-5

z
x

y

(b)

(c)

-5.6
-5.8
-6.0
-6.2
-6.4
-6.6
-6.8
-7.0
-7.2
-7.4 -5
10

a face (2nd)m face (2nd)C face (2nd)

10-4

10-3
PCO (atm)

10-2

10-2

10-1

Figure 2: Formation energies Eform defined
Eq. (1) for interface with CO bonds withrespect to partial pressures of CO and N2 .
Reprinted from Ref. 3.

z
z
x

y

x

those along the a, m, and Si faces. The formation energy of the nitride layer at the topmost
layer is smaller than that at the second layer,
resulting in the localization of the N atoms at
the interface. Note that the NO annealing generates the nitride layers immediately below the
SiO2 layers without any transition layers. It
was found that the eﬀect from the SiO2 layeron the interface atomic structure after the NO
annealing and our conclusions derived by thesurface models do not change when the SiO2
layers are attached to the substrates.

yFigure 1: Interface atomic structures with CO
bonds for (a) a-, (b) m-, and (c) C-face models before N-atom incorporation. Blue, brown,
red, and gray spheres are Si, C, O, and H
atoms, respectively. Black lines are the boundaries of the supercell.
where E(SiO2 ) is the total energy of a SiO2
(w/o mod)unit in a bulk of quartz SiO2 . Etotal
and(w/ mod)
Etotalrepresent the total energies of theinterface without and with N-atom incorporation, respectively. In addition, µNO , µCO ,and µN are the chemical potentials of a NO
molecule, a CO molecule, and N atoms in a N2
molecule, respectively. We set the temperature
at 1000 K and the partial pressure of NO gas(pNO ) at 1 atm. The partial pressures of CO(pCO ) and N2 (pN2 ) gases were set at 10−5 atm
and 0.25 × 10−5 atm.
The formation energies of the nitride layers are shown in Fig. 2. All the reactions
are exothermic and the nitride layers growing
along the a face are the most stable among

References[1] T. Ono and K. Hirose, Phys. Rev. Lett.
82, 5016 (1999).
[2] K. Hirose, T. Ono, Y. Fujimoto, and
S. Tsukamoto, First Principles Calculations in Real-Space Formalism, Electronic
Configurations and Transport Propertiesof Nanostructures (Imperial College, London, 2005).
[3] N. Komatsu, M. Ohmoto, M. Uemoto,and T. Ono, J. Appl. Phys. 132, 155701(2022).

54

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First-Principles Study on Carrier Mobility of SiC6
Nanoribbons
Yoshiyuki EGAMI
Division of Applied Physics, Faculty of Engineering, Hokkaido University
Kita 13, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-8628

In recent years, as semiconductor electronic
devices have become smaller, a remarkable reduction in carrier mobility due to the thinningof silicon channels has become an important
issue. Therefore, two-dimensional materials
that can maintain high carrier mobility evenwith thin ﬁlms have been attracting attention.
A typical 2-dimensional material, graphene,
has a carrier mobility of about 100 times thatof silicon, and is therefore expected to becomea post-silicon material. However, since the energy band gap of graphene is zero, it is diﬃcult to apply it to semiconductor devices. So,
several studies have been performed to open
the gap. Among them, this work focuses onsiligraphene, in which graphene is doped withsilicon atoms. In particular, it is shown that
SiC6 siligraphene has a negative Poisson’s ratio
and a unique property that the carrier mobilitychanges signiﬁcantly with strain. On the other
hand, the carrier mobility of SiC6 nanoribbons
fabricated by cutting SiC6 siligraphene as achannel material has not yet been clariﬁed.
In this work, the carrier mobility of SiC6
nanoribbon considering acoustic phonon scattering at room temperature is evaluated using
the eﬀective mass, elastic modulus, and deformation potential obtained by ﬁrst-principles
calculations [1]. As a result, the electron
mobility of SiC6 nanoribbons is signiﬁcantly
lower than that of SiC6 siligraphene, but thehole mobility is suﬃciently improved to be applied to n-type transistor materials. For future
work, it is necessary to investigate the depen-

dence of carrier mobility on the width and edge
structure of the nanoribbon.
Ballistic electron transport properties in
SiC6 nanoribbons connected to semi-inﬁnite
electrodes consisting of graphene are also evaluated [1, 2]. There are two types of conduction channels: in the 1st channel, the scattering wave function is extended throughout thesystem via the delocalized π orbitals of carbon atoms (Fig. 1(a)), resulting in a conduction channel with high transmission. On theother hand, in the 2nd channel, the scatteringwave propagates via states consisting of sp3 orbitals of silicon atoms, but most of the wave is
reﬂected at nanoribbon region (Fig. 1(b)) andthe transmission is quite small.
This work has been performed on System Bof the Supercomputer Center, the Institute for
Solid State Physics, the University of Tokyo.
(a)

(b)

Figure 1: Charge density distribhutions of
scattering wave functions.

References[1] K. Hirose et al.: First-principles calculations in real-space formalism (Imperial
College Press, 2005).
[2] Y. Egami, S. Tsukamoto and T. Ono:
Phys. Rev. Res. 3, 013038 (2021).

55

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Analyses related to atomic structures and atom
dynamics at complex structures such as surfaces,interfaces and defects
Satoshi WATANABE
Department of Materials Engineering, the University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656

1

Introduction

To deepen our understanding on atomic arrangements and dynamics in/at complex structures such as surfaces, interfaces and defects
is crucial for further development of novel
information and energy devices. We havebeen tackling this issue using various simulation methods including multi-scale or machinelearning approaches. In the following, two of
our results in fiscal year 2022 are described.

2

Defect structures in WS2
thin films

Investigations on WS2 aiming at electronic devices have been active recently. However, theperformance of prototype devices were much
lower than expected because of structural defects in fabricated WS2 thin films. We examined [1] defect structures of WS2 thin films using the high-dimiensional neural network potential (HDNNP). We successfully generated
HDNNP with the root mean square error ofenergy from density functional theory (DFT)
calculation of about 6 meV/atom. Molecular
dynamics (MD) simulations using the HDNNP
and WS2 slab models reveal that S vacancies introduced in the slab are transformed
into structures consisting of 5-memberd and 9membered rings or other structures including
rings diﬀerent from 6-membered one spontaneously.

3Electrical conductivity andSeebeck coeﬃcient of organic semiconductors

Organic semiconductors have attracted muchattention recently as thermoelectric materials.
However, their power generation eﬃcency is
still insuﬃcient for practical applications. We
developed [2] a realistic evaluation method of
the electrical conductivity and Seebeck coefficient of organic semiconductors using electronic structure calculations based on DFT.
In our method, many structures were obtainedusing MD simulations with an empirical force
field to consider thermal fluctuation in a crystal explicitly. The Seebeck coeﬃcient and carrier mobility were obtained from the averageof the density states and transfer integrals between adjacent moleculres calculated for eachstructure, respectively. We applied our scheme
to pentacene and rubrene. Our results agree
well with experimental data.

References[1] R. Otsuka, K. Shimizu, H. Wakabayashi,
S. Watanabe: 70th JPSJ Spring Meeting
2023, 16p-B414-7 (in Japanese).
[2] M. Ohno, K. Shimizu, and S. Watanabe:Appl. Phys. Express 16 (2023) 011005.

56

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Theoretical study of point defects invisible-light-driven semiconductor photocatalysts
using first-principles calculations
Seiichiro L. TEN-NO
Graduate School of System Informatics, Kobe University
Rokkodai-Cho, Nada-Ku, Kobe 657-8501

Point Defect in Layered Perovskite Photocatalyst Y2 Ti2 O5 S2
Layered perovskite Y2 Ti2 O5 S2 (YTOS) isa strong candidate for semiconductor photocatalysts for overall water splitting undervisible light. Although YTOS has a band
gap sutable for absorption of visible light andvalence/conduction band positions useful for
overall water splitting, structural defects during synthesis should be controlled to promote
photocatalytic performance [1, 2, 3].
We have investigated the point defects andelectronic structures using first-principles calculations based on the density functional theory (DFT). The DFT calculations were performed within the PBE+U (Perdew–Burke–
Ernzerhof functional with Hubbard U correction) functional using the projector augmented
wave (PAW) method, as implemented in the
Vienna ab initio simulation package (VASP).
We have evaluated the formation energy ofanion vacancies Eform using the chemical potentials ∆µi relative to bulk precipitate. ∆µiwere determined by the phase diagram of therelevant compounds in the synthesis of YTOS.
Fig. 1(a) shows the phase diagrams as functions of ∆µTi and ∆µY for S-rich conditions,in which YTOS is stable within the area of the
ABC triangle. In Fig. 1(b), Eform of VS , VOc ,and VOab for S-rich conditions is shown as afunction of the Fermi level EF measured fromthe valence band maximum EV . The evalu-

(a)

A

C

B
S-rich:ΔμS = 0 eV(b)+2/+1 +1/0
+1/0

+2/+1

+1/0

+2/+1

S-rich:ΔμS = 0 eV

Figure 1: (a) Quaternary phase diagram of
YTOS with respect to ∆µTi and ∆µY for Srich conditions. (b) Formation energy of anion
vacancies Eform against EF − EV .
ation of Eform suggests that the S-defect (Odefect) is easy to form when EF is near thevalence (conduction) band.
Chemical Doping in Perovskite Photocatalyst SrTiO3Perovskite SrTiO3 (STO) is a typical semiconductor photocatalyst for overall water split-

57

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

ting under ultraviolet. According to the recent
experiment [5], it is observed that Al-doped
STO can enable a quantum eﬃciency of almost
unity at wavelengths between 350 and 360 nm.
To explore more eﬃcient dopants for higherperformance photocatalysts, we have studied
the dopant eﬀects on the electronic structuresof STO using the first-principles analysis. The
DFT calculations were performed within the
PBE+U functional using PAW method as implemented in VASP. We treated the supercell
models including one VO and two XTi (oneMgTi ) with X being B, Al, Ga, In, Tl, Sc.

ABCD points.
Water oxidation reaction on BiVO4
Perovskite BiVO4 (BVO) is used as an O2 evolution photocatalyst for overall water splitting. We have investigated the reaction processes of the water oxidation on BVO using
the first-principles calculations. The DFT calculations were performed within PBE and revised PBE (RPBE) using the slab models with(110) facet. Fig. 3 shows the energy diagramof O2 - and H2 O2 -evolution on BVO (110) surface, which suggests that H2 O2 -evolution is expected to be favorable on the (110) facet compared to O2 -evolution.

(a)

(b)

Figure 2: (a) Ternary phase diagram of STO
with respect to ∆µSr and ∆µTi . (b) Dopant
formation energy of each dopnat Ef along
ABCD points.

Figure 3: Energy diagram of O2 - and H2 O2 evolution

References[1] Q. Wang et. al.: Nat. Mater. 18, 827(2019).

The supercell calculations showed that each
dopant except for Tl can cancel the defect levelarising from the VO . We have also evaluatedthe formation energy of each dopant Ef using
the chemical potentials ∆µi relative to bulk
precipitate. ∆µi were determined by the phase
diagram of the relevant compounds of STO.
Fig. 2(a) shows the phase diagrams as functions of ∆µSr and ∆µTi , in which STO is stable within the area of the ABCD square. In
Fig. 2(b), Ef of each dopant is shown along[2] Z. Pan et. al.: Res. Chem. Intermed. 47,
225 (2021).
[3] M. Nakabayashi, K. Nishiguchi et.
al.:
J. Phys. Chem. C (in press:
https://doi.org/10.1021/acs.jpcc.3c00820).
[4] M. Cococcioni et. al.: Phys. Rev. B 71,
035105 (2005).
[5] T. Takata et. al.: Nature 581, 413 (2020).

58

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Development of Thermal Functional Materials Based on First-principles
Simulations
Wenyang DING1, and Junichiro SHIOMI1,2
1. Department of Mechanical Engineering, The University of Tokyo, Japan
2. Institute of Engineering Innovation, The University of Tokyo, Japan
Manipulating thermal conductivity of crystalline materials by phonon engineering has attracted tremendous attention.
Van der Waals (vdW) heterostructures are an ideal candidate to realize low thermal conductivity in their throughthickness direction due to its weak interlayer interactions and prominent disorder by alternating stacking orders.1
However, previous studies have not been able to characterize phonon propagation thought vdW heterostructures in modewise fashion, which has limited the understanding and engineering of the heat conduction. Herein, for the first time, heat
conduction in vdW heterostructures are resolved to the level of individual phonon polarizations, whose incident angles
are utilized to elucidate the role that phonon interference and phonon tunneling play in vdW heterostructures.
First, we searched for vdW graphene-WS2 heterostructure with the ultralow thermal conductivity by combining
Bayesian optimization and non-equilibrium molecular dynamics (NEMD) including all the orders of anharmonicity. It isfound that the lowest thermal conductivity of optimized heterostructure is 0.04 W/m-K, only 5% of graphite thermal
conductivity and 13% of pristine WS2 thermal conductivity (Fig. 1). Interestingly, the thermal conductivity of optimized
heterostructure possesses only half of the thermal conductivity of the corresponding periodic superlattice. Moreover, thelattice thermal conductivity of pristine WS2, pristine graphite, periodic superlattice, and the optimized heterostructure arealso calculated by utilizing mode-resolved atomistic Green’s function (AGF) method. Since AGF only considers
harmonic interatomic force constants, it serves to probe the characteristics of elastic phonon scattering whose influenceon thermal conductivity is partial but significant as can be seen in the comparison with NEMD results (Fig. 1). To further
study the characteristics of elastic phonon scattering, the mode-resovled phonon transmissions are calculated using the
S-matrix analysis.2

References(1) Liu, Y.; Weiss, N. O.; Duan, X.; Cheng, H.-C.; Huang, Y.; Duan, X. Van
der Waals heterostructures and devices. Nature Reviews Materials 2016, 1(9), 16042.
(2) Ong, Z.-Y. Atomistic S-matrix method for numerical simulation ofphonon reflection, transmission, and boundary scattering. Physical Review B
2018, 98 (19), 195301.

Fig. 1 Comparison of the thermal conductivityof difference. structures and methods.

59Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First-principles study of energy and spintronics
materials
Fumiyuki ISHII, Naoya YAMAGUCHI, Rifky SYARIATI,
Hikaru SAWAHATA, Hana Pratiwi KADARISMAN, Kaiki SHIBATA,
Yaotang ZHANG, Vierta SARASWATI, Takahiro TOMINAGA,
Yedija Yusua Sibuea TEWENG, Juhri HENDRAWAN,
Yume MORISHIMA, Khusnul YAKIN, Wardah AMALIA,
Syifa Fauzia Hariyanti PUTURI, Ahmad GHIFFARI, Salsabila Amanda PUTRI
Nanomaterials Research Institute, Kanazawa University
Kanazawa, 920-1192, Japan

1. MnBi2 Te4 van der Waals layers[1]The transverse thermoelectric eﬀect, which
produces an electric ﬁeld in the direction of
the cross product of the temperature gradientand magnetization of magnetic materials, has
attracted much attention in recent years because of its potential applicability to a large
area. Since MnBi2 Te4 was predicted to haveantiferromagnetic order in the interlayer and to
be a ferrimagnetic semiconductor with quantized anomalous Hall conductivity in an odd
number of three or more septuple layers, it isexpected to generate a transverse thermoelectric eﬀect. We have performed ﬁrst-principles
calculations by using OpenMX code[2] for ultrathin MnBi2 Te4 ﬁlms and estimating thecoeﬃcient representing the magnitude of thetransverse thermoelectric eﬀect. The anomalous Hall and Nernst eﬀect were calculated byusing our developed eﬃcient methods by local Berry phase in momentum space[3]. A
large enhancement in transverse thermoelectric eﬀect, anomalous Nernst coeﬃcient, larger
than 20µV/K, was obtained by the Seebeck
term for a wide range of carrier concentrations.
The present results motivate further studies on
the anomalous Nernst eﬀect in intrinsically or
doped magnetic semiconductors.

2. Hapkeite Fe2 Si[4]In order to obtain a guideline for exploringnew inexpensive and high-performance thermoelectric materials, the ﬁrst-principles calculations were performed to clarify the thermoelectric properties of Fe-Si alloys, which
are the most abundant iron-based compounds.
As a result, we obtained a large transverse
thermoelectric transport coeﬃcient αxy of 2.06
A/mK for the model crystal structure of amineral Hapkeite Fe2 Si contained in a meteorite Dhofar 280, which is believed to originate from the Moon. Calculated results showthat the model structure, Hapkite Fe2 Si, exhibits a more signiﬁcant transverse thermoelectric transport coeﬃcient than bcc-Fe andhexagonal Fe2 Si. Detailed analysis of the electronic states and transport coeﬃcients indicates that the signiﬁcant transverse thermoelectric eﬀect is due to an anomaly in the density of states (DOS) at the interface between
the Fe layer and the Si-Fe alternating layer.
The band-edge type van Hove singularity contributed to the DOS at the Fermi level. Several previous studies have attempted to generate transverse thermoelectric eﬀect throughmulti-layered structures with each layer on theorder of 1 nm scale. A similar interfacial eﬀect
that enhances the transverse thermoelectric ef-

60

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

fect may be observed. Therefore, this ﬁnding
is promising for the design of new transverse
thermoelectric materials.

References[1] Y. Morishima, N. Yamaguchi, H. Sawahata, and F. Ishii, Appl. Phys. Express,
16 043003 (2023).

3. Persistent spin helix in diamond[5]Density functional theory (DFT) simulations
were performed to investigate the diamond(111) surface. We investigated how diﬀerent
terminations such as hydroxyl (OH) and hydrogen (H) aﬀect the strength of the spinorbit coupling on the diamond surface and thespin structure in momentum space. The calculation results reveal that the OH-terminatedstate contributes to the valence band maximum (VBM), while the H-terminated state
contributes to the conduction band minimum(CBM). Due to the OH termination, persistent spin helix spintexture is obtained and thespin-orbit coupling coeﬃcient is found to be
14.2 meV·Å at the valence band maximum(VBM). This is signiﬁcant compared to theZnO(101̄0) surface and slightly larger than various n-type sphalerite quantum wells. Moreover, it is larger than the 3.6 meV·Å resulting from the hydrogen termination of diamond.
Due to the large spin-orbit coupling coeﬃcient,
the wavelength of persistent spin helix is shortened, and miniaturization of spin ﬁeld eﬀect
transistors can be expected.

[2] H. Sawahata, N. Yamaguchi, S. Minami,
and F. Ishii, Phys. Rev. B 107, 024404(2023).
[3] T. Ozaki et al., http://www.openmxsquare.org/[4] T. Tominaga N. Yamaguchi, H. Sawahata, and F. Ishii, Jpn. J. Appl. Phys. 62
SD1019(2023).
[5] H.P. Kadarisman, N. Yamaguchi, and
F. Ishii, Appl. Phys. Express, 16023001(2023).

4. The Rashba eﬀect of PbS/Au(111)We have performed the ﬁrst-principles calculation of the PbS(111) which has buckled honeycomb structure on the Au(111) surfaces. The
broken inversion symmetry due to the bucklingof the system shifted the energy and caused the
Rashba eﬀect. The orientation or contact at
the interface highly controls the Rashba eﬀect
at heterostructures. For the metal-metal contact, Pb-Au, allows the Rashba eﬀect at theinterface, while metal-insulator contact, S-Au,
does not. The PbS(111) can be used to generate the Rashba eﬀect surface state of thin
noble metals.

61

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First Principles Studies on Spin Tranfer Torque Magnetic
Randum Access Memory (STT-MRAM)
Kenji SHIRAISHI
Institute for Materials and Systems for Sustainability,
Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601
Spin-transfer

torquemagnetic

random-magnetic anisotropy. In the DOS peaks in theaccess memory (STT-MRAM) is expected to be

down spin channel above Fermi energy in ideal

a non-volatile memory that replaces SRAM andmodel, the wave functions mainly form the

DRAM currently in use. MgO layer inbonding orbitals between interfacial Fe atoms

CoFeB/MgO/CoFeB

ofand second layer Fe atoms. Near grain

irregularly shaped grains with a diameter of 10

boundary, the in-plane symmetry is broken by

to 15 nm, and the presence of mainly the Σ5

the grain boundary and the hybridization(210) [001] grain boundaries was observed at

between interfacial Fe atoms and second layer

the interface between the grains. iPMA isFe

generally known to be sensitive to theunoccupied down spin states of “|m|=1” extend

cleanliness of the interface between theabove Fermi energy. We suggest that it isferromagnetic layer and the insulator [1]. In thisnecessary to keep the d orbital hybridization

study, we focus on the grain boundary whicharound Fe atoms for further microfabrication.

MTJ

consists

atoms

weakens.

It

causesthattheoccurred most frequently in MgO film andinvestigated the influence of the grain boundary
for iPMA by performing the first principles
calculations.
Near grain boundary (within a 4Å range
from the center line of grain boundary, theFig.1: Wave functions at Γ point in down spin

magnitude of iPMA strongly decreases. It is

statesbecause the DOS peaks in the down spinchannel for “|m|=1” above Fermi energy are

References

decreased. It causes the spin orbit coupling[1] A. Hallal, H. X. Yang, B. Dieny, and M.

hybridization

Chshiev, Phys. Rev. B 88, (2013) 184423.

of(|1|×|1|)changesfromperpendicular magnetic anisotropy to in-plane

62

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First Principles Studies on Si-rich a-SiN for Flash Memory
Application
Kenji SHIRAISHI
Institute for Materials and Systems for Sustainability,
Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601
SiN is a useful material under harshfound π state is unprecedented in the sense thatenvironments due to its high thermal stability

it has the origin of the floating state distributed

and toughness against fracture, and lately

not around atomic sites but in the internal space

utilized as passivation layer in Si solar cells. Toof the material. The multiple charge states weadvance our understanding of the memory

have found may be utilized as an active element

function in SiN, however, the most important

in information technology.

task never achieved so far is the determinationof the thermodynamic level (TD) induced by
VN. This TD level is responsible for theelectroncaptureandemissionandits

determination involves assessment of roles ofhybridization, spin polarization and ionicity of

Fig.1: π-like state appeared in VN in Si3N4

VN in SiN. We find that the appearance of thisTD level is promoted by the spontaneous
structural transformation near VN, resulting in
multiple charge states of nitrogen vacancy.
We have performed the density-functional
calculations with the hybrid approximation thatclarify the atomic and electronic structures of
the N vacancy (VN) in Si3N4. We have foundFig.2: formation energy of VN in Si3N4.

that the floating state hidden in the conduction
bands create a new π-like state in collaboration

Referenceswith the dangling-bond states around the VN(,

[1] F. Nanataki, K. Shiraishi, J. Iwata, Y.

(Fig.1) thus rendering the VN being multiple

Matsushita, A. Oshiyama, Phys. Rev. B 88,

charge states from +1 to -5 (Fig.2). The newly(2013) 184423.

63

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Hydrogen production by water splitting on CeO2 (100) facet
Akira YOKO
Advanced Institute for Materials Research,
Tohoku University, Katahira 2-1-1,Aoba-ku, Sendai, Miyagi 980-8577
CeO2 nanocubes with (100) facet have

For the (100) facet model, half of the oxygen

been synthesized by supercritical method, andwas moved to the other side to exclude polarity

it was found to have high oxygen storage

keeping the CeO2 composition. When CeO2 iscapacity. In this study, the redox property of the

fully hydroxylated by water adsorption and(100) facet was studied by using first-principles

dissociation, the surface state is changed as

calculations.

Figure 1(b). Interestingly, the lattice expansion

In this study, first principles simulations

observed inside of the bare surface model (Fig.

based on density functional theory (DFT) was

1(a)) was relaxed with surface hydroxylation

conducted. Plane wave basis DFT simulations

owing to the surface stabilization.

were conducted using VASP code. Structures of
CeO2 at surface and bulk were calculated withgeneralized gradient approximation proposedby Perdew, Burke, and Ernzerhof (PBE) [1].
The

valenceconfigurations

of

the

pseudopotentials were 5s25p64f15d16s2 for Ce
and 2s22p6 for O. The DFT+U method
introduced by Dudarev et al. [2] was used totreat electron localization. The parameter U-J
was set to 5.0 eV for Ce4f and 5.5 eV for O2p
states, respectively, based on previous studies

Fig.

of CeO2 [3]. The Monkhorst-Pack k-point meshhydroxylated (100) surface.

of 4×4×4 was used for the unit cell of CeO2.

References

The slab model exposing (100) facet includes[1] J.P. Perdew, K. Burke, M. Ernzerhof, Phys. Rev.

60 atoms, and that for (111) facet includes 108

Lett., 77 (1996), p. 38650. [2] S. Dudarev, G. Botton, S.

atoms. The Monkhorst -Pack k-point mesh for

Savrasov, C. Humphreys, A. Sutton, Phys. Rev. B, 57

the slab model was 4×4×1.

(1998), p. 1505. [3] P.R. Keating, D.O. Scanlon, B.J.

Figure 1(a) and 1(b) show CeO2 (100) bare

1:

CeO2

(100)

bare

surfaceandMorgan, N.M. Galea, G.W. Watson, J. Phys. Chem. C,

surface and hydroxylated surface, respectively.

116 (2012), pp. 2443-2452

64

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Electronic structure analysis of SiC/SiO2 interface under gatedelectric field
Yu-ichiro MATSUSHITA
Laboratory for Materials and Structures, Institute of Innovative Research,
Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo 152-8552
SiC-MOS devices have attracted muchas the (11-20) plane. We found that thisattention as next-generation power devices due

is related to the structural channel nearto their excellent characteristics. However, thethe interface. Of particular importance,

device characteristics are at most 10% of thethe localization of the wavefunction attheoretical performance value of SiC, which is

this (0001) interface is distributed muchthought to be due to high-density interface

closer to the interface (<5 Å) thandefects. However, we have proposed a new

expected

type of mechanism for the generation of

approximationinterfacial levels, which originates from theexpected to be more sensitively scatteredpeculiarity of the wave function at the lower

by interface defects. This result may

end of the conduction band of SiC.

explain why the channel mobility of the

from

the

effective(EMA),

and

mass

thus

is

(0001) plane is particularly low comparedIn this study, we have performed atheelectronic

to the other planes (e.g., (1120)), which is

structure of the SiC/SiO2

interfaces

well known from experiments.

comparative

study

of

under a gated electric field by means oftheoretical calculations based on density
functional

theory,includingthedependence of the electronic structure on
the plane orientation. It is found [1] that

FIG 1. Isosurface at 38% of the peak value

the wavefunction at the conduction bandof wavefunction localized at outermost

minimum (CBM) at the SiC(0001)/SiO2

cubic site (F = 1.0 MV/cm, CBM). The blue,

interface tends to be localized at thebrown, red, and small white balls depict

cubic site closest to the interface. On the

Si, C, O, and H atoms, respectively.

other

hand,

we

found

that

such

localization of the wavefunction cannotWe

be observed on nonpolar interfaces such

65

have

also

clarified

that

the

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyodeviation from the EMA, which is a

succeeded in proposing a new way tofrequently used method to analyze theimprove SiC-MOS devices.

electronic structures in semiconducting
devices,isattributed

to

the

long

structural periodicity along the [0001]
direction in the SiC crystals.
Furthermore,in

actual

SiC-MOSinterfaces, the introduction of nitrogen isused to improve the interface properties.
However, its microscopic mechanism has
not

been

elucidated.

In

the

experimental

considering

recentFIG 2. Local density of states (LDOS) of

addition,

the CBM in slabs with di↵erent N-doped
regions. For clarity, averaging was done

results that the introduced nitrogen also

over the atomic layer thickness period.

diffuses to the SiC side. We study how

Applied electric fields are 0, 5, and 10

the electronic states at the SiC / SiO2

MV/cm, respectively.

interface are affected when nitrogen is
introduced as a dopant based on first-

References

principles calculations. As a result, we[1] K. Tachiki, Y. Nishiya, J.-I. Iwata, and Y.

found that the deep doping of nitrogen

Matsushita, arXiv:2303.08527 (2023).

relaxes the confinement potential near[2] H. Yoshioka, J.-I. Iwata, and Y. Matsushita,

the interface and the localization of the
wavefunction,

which

is

expected

toimprove

deviceproperties.

We

the

arXiv:2303.05085 (2023).

Fig. 1: Supercomputer System B at SCC-ISSP.

66

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Density functional theory study of adsorption and reactionof molecules on metal surfaces
Ikutaro Hamada
Department of Engineering, Graduate School of Engineering,Osaka University, 2-1 Yamada-Oka, Suita, Osaka 565-0871
mechanism of ORR on SAC is not yet fully
resolved. In this study [4], we focus on theexchange-correlation functional to be used to
study ORR on SAC, in particular Fe and Co
SAC embedded in a nitrogen-doped graphene(Fe/Co-N4-C). By choosing the results obtainedusing the Bayesian error estimate functionalwith van der Waals correlation (BEEF-vdW)[5] as references, we perform systematical
calculations of the ORR intermediates on FeN4-C and Co-N4-C using commonly usedsemilocal exchange-correlation functionals
with and without dispersion correction and findthat revised Perdew-Burke-Ernzerhof (RPBE)[6,7] functional with Grimme’s dispersion
correction [8] (RPBE-D3) gives the results
consistent with those obtained using BEEFvdW. The conclusion drawn in this work as
well as the strategy taken there will be useful tochoose an appropriate (low-cost) exchangecorrelation function to study electrochemicaland heterogeneous reactions.

Graphene doped with foreign elements has
attracted enormous attention because of
emergent electronic properties, besides its own
unique structural, electronic, and mechanicalproperties, as well as its stack(s) with the same
and/or different two-dimensional materials.
Graphene doped with nitrogen has beenshown to exhibit catalytic activity toward
electrochemical oxygen reduction reaction(ORR). To understand the mechanism of ORR
on nitrogen-doped graphene, it is important tounderstand the interaction between grapheneandwater,
acommonsolvent
inelectrochemistry. However, density functional
theory with the conventional semilocal
approximation fails to describe the weak watergraphene interaction accurately. In this work [1],we use rev-vdW-DF2 [2], one of the van der
Waals density functionals, which has beenshown to be able to describe the watergraphene interaction accurately [3], to study theinteraction between water molecule andnitrogen-doped graphene. We consider different
water orientations and different nitrogenconfigurations and obtain the preferredorientations of the water molecule, dependingon the nitrogen configuration. We reveal thatgraphitic and pyridinic nitrogen atoms arecharged positively and negatively respectively,
which lead to different water orientations. Theinteraction energy curves obtained in this work
may serve as references for future studies andbe useful for modeling of graphene/waterinterfaces
withhybridquantummechanics/molecular mechanics-type methods.
Nitrogen-doped graphene with a single
transition metal atom (single atom catalyst,
SAC) has also been attracted much attention as
it shows ORR activity comparable to platinum,and there have been numerous studies bothexperimentally and theoretically. However, theReferences[1] A. F. Z. Abidin and I. Hamada, Phys. Rev.
B 105, 075416 (2022).
[2] I. Hamada, Phys. Rev. B 89, 121103 (R)(2014).
[3] J. G. Brandenburg, A. Zen, D. Alfé, and A.
Michaelides, J. Chem. Phys. 151, 164702(2019).
[4] A. F. Z. Abidin and I. Hamada, Surf. Sci.
724, 122144 (2022).
[5] J. Wellendorff, et al., Phys. Rev. B 85,
235149 (2012).
[6] J. P. Perdew, K. Burke, and M. Ernzerhof,
Phys. Rev. Lett. 77, 3865 (1996).
[7] B. Hammer, L. B. Hansen, and J. K.
Norskøv, Phys. Rev. B 59, 7413 (1999).
[8] S. Grimme, J. Antony, S. Ehrlich, and H.
Krieg, J. Chem. Phys. 132, 154104 (2010).

67

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Theoretical determination of charge injection levels
in organic semiconductor films
Ryo Uza and Susumu Yanagisawa
Department of Physics and Earth Sciences, University of the Ryukyus
Senbaru 1, Nishihara Okinawa 903-0213

Zinc phthalocyanine (ZnPc) can be used as ap-type semiconductor with hole carriers. Fluorinated zinc phthalocyanine (F16 ZnPc) can
also be used as an n-type semiconductor.
ZnPc and F16 ZnPc are attracting attention
as organic solar cell materials. Fundamental understanding on the formation mechanism of charge injection levels is urgent for
their practical application. In the present
work, Vienna Ab-initio Simulation Package(VASP) was used to determine from firstprinciples the lattice parameters of the crystals and the atomistic structure of molecular films by the van der Waals (vdW) density functional[1]. Subsequently, the band energy calculations were performed with the GW
approximation, which included the eﬀects ofelectronic polarization. The ionization potential (I) or electron aﬃnity (A) of any molecule
depends on the electronic polarization of thesurrounding molecules and the electrostaticinteractions upon the ionized molecule fromits surroundings[2]. The eﬀect induced by
the molecules surrounding an ionized molecule,which stabilizes the charge injected into themolecule by the polarization clouds with an opposite charge, is called electronic polarization.
In this work, both the eﬀects of the electrostatic interaction and the electronic polarization were included in the calculations to quantitatively predict the level of charge injection.
The result indicates that in case of ZnPc thinfilm the energy levels are relatively shifted up
in the standing configuration and shifted downin the lying configuration. This comes fromthe electric quadrupole moment of the neutral
molecule. Electric quadrupoles generate electrostatic potentials in crystals and films. The
generated electrostatic potential is governedby the electric quadrupole tensor and depends
on the distance between molecules and theorientation angle of the constituent molecule.
Therefore, the shift is opposite for the standing and lying configurations, as the orientation
angle of the molecules relative to the nearest
neighbor molecules changes and the sign of theelectrostatic potential is diﬀerent. Similarly,
given the opposite signs of the zz component of
the electric quadrupole tensor, the signs of theelectrostatic potential for ZnPc and F16 ZnPc
are opposite.
The eﬀect of electronic polarization was determined using the GW approximation to calculate I and A, and the density of states was
obtained by matching the corresponding energy levels to the peak of the density of states.
It was found that the positions of the densityof states peaks are in good agreement with theexperimental photoelectron/inverse photoelectron spectra[3] (Fig. 1). The calculated energy
gap is slightly shallower than the experimental
results, which can be corrected by improving
the accuracy of the GW approximation.
Overall, it is concluded that the present theoretical method is of practical use in predicting the frontier energy levels of organic semiconductor thin film. Prediction of the charge
injection levels in mixed films of ZnPc and68

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

F16 ZnPc is in progress.

This work

This work

This work

This work

Figure 1: Theoretical density of states (DOS)of ZnPc and F16 ZnPc thin films with standing and lying configurations. The experimental DOS measured with direct/inverse
photoemission[3] are also displayed.

References[1] I. Hamada: Phys. Rev. B 89, 121103(2014).
[2] K. Yamada, S. Yanagisawa, T. Koganezwa, K. Mase, N. Sato, and H.
Yoshida: Phys. Rev. B 97, 245206 (2018).
[3] Y. Uemura, S. A. Abd-Rahman, S. Yanagisawa, and H. Yoshida: Phys. Rev. B 102,
125302 (2020).

69

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Reduction of Rare Metals in Fuel Celland Formic Acid Decomposition Catalysts
Norihito Sakaguchi
Center for Advanced Research of Energy and Materials, Faculty of Engineering,
Hokkaido University, Sapporo, Hokkaido 060-8628
We investigated the adsorption and diffusion

substrates. We revealed that the ferromagnetic

properties of single-atom catalysts on light-

electronic structures of metal substrates are notelement doped graphene supported on the metal

required to enhance the chemical bonding

substrates, with the aid of the first-principles

betweencalculation based on the density functional

element doped graphene light-element doped

theory (DFT). We also investigated the catalytic

graphene. In most cases of steric light-element

activities of the considered systems in oxygen

doped graphene, the non-magnetic substrates

reduction and formic acid decomposition

also weaken the Pt and Cu adsorption andreactions.

enhance the catalyst diffusion on the light-

At first, we investigated the stabilities ofsingle-atom

catalysts

andlight-

element doped graphene in the same way as the

single Pt and Cu atoms on light-element doped

magnetic substrates.

graphene supported on the metal substrates. We

Based on the previously reported volcano

considered Fe, Ni, and Cu as metal substrates.

plots, we also investigated the catalytic

We performed the total energy and electronic

activities of the considered systems in oxygen

structure calculations using The Vienna Ab

reduction and formic acid decomposition

initio Simulation Package (VASP). We installed

reactions from the adsorption energies of thea parallelized VASP with Intel® MPI Librarycomponents of the reactants. Although theand Intel® Math Kernel Library. We havestrong chemical bonds between catalysts andreported that the adsorption of Pt and Cu atoms

supports are expected to lead to higher catalyticon light-element doped graphene with flat

activities,

structures became stronger through supporting

reactions have been found only in a few

the magnetic substrates, i.e., Fe and Ni

systems.

theenhancementsofcatalytic

substrates, last year. We reinvestigated theadsorption properties of Pt and Cu atoms on

References

light-element[1] Y. Kunisada, N. Sakaguchi, Nano

dopedgraphene

with

flat

structures on non-magnetic substrates, i.e., Cu

Express 3 (2022) 042001.

70

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Development of Efficient Oxygen Storage Materials
and Hydrogen Permeation Barrier Materials
Yuji KunisadaCenter for Advanced Research of Energy and Materials, Faculty of Engineering,
Hokkaido University, Sapporo, Hokkaido 060-8628
We investigated the doping effects on the

We also investigated the hydrogen isotope

oxygen absorption/desorption properties ofsolubility and diffusion properties in thebrownmillerite-type oxides,[1] with the aid of

amorphous ceramic materials. We considered

the first-principles calculation based on the

Al2O3 as the hydrogen permeation membrane.

density functional theory (DFT). We also

We performed melt-quenching technique toperformed O K-edge electron energy loss

obtain the amorphous structure last year.

spectroscopy (EELS) simulations to build the

However, the obtained structures are not fully

EELS

the

in the amorphous phase due to insufficientexperimental EELS spectra obtained withmelting. Therefore, we obtained the atomic

transmission electron microscopy. Besides, thestructures of the amorphous Al2O3 using higher

solubility and diffusion properties of hydrogenmelting temperatures and longer time steps forisotopes in amorphous ceramic materials weremolecular dynamics simulations. Next, we

also investigated to validate its application as

performed 2000 DFT calculations with varioushydrogen permeation barrier materials.

hydrogen positions in amorphous Al2O3 todatabaseforcharacterizing

We investigated the oxygen absorption

determinetherelationshipbetweentheenergies with various heteroatom substitutions

dissolution energies and the local structures.

in Ca2AlMnO5 using The Vienna Ab initio

Using the Voronoi analysis, we evaluate theSimulation Package (VASP). We installed avolume occupied by the hydrogen atoms.

parallelized VASP with Intel® MPI Library and

Finally, we revealed the dependence of theIntel® Math Kernel Library. We revealed thedissolution energies and diffusion barriers on

effects of local atomic distortions induced by

the volume occupied by the hydrogen atoms.

dopants on the oxygen absorption energies. We

References

also

performed

simulationsof[1] K. Tanahashi, Y. Omura, H. Naya, Y.

using

Z+1

Kunisada, N. Sakaguchi, A. Kurniawan, T.

approximation to construct the database of the

Nomura, ISIJ International 62 (2022)

O K-edge EELS spectra.

2578-2586.

nonequivalent

EELS
O

atoms

71

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Densest binary/ternary sphere packings andprediction of quaternary hydrides
Ryotaro Koshoji, Masahiro Fukuda, Mutsuaki Kawamura, Taisuke Ozaki
Institute for Solid State Physics, University of Tokyo
Kashiwa-no-ha, Kashiwa, Chiba 277-8581

It was widely accepted in the 2000s that the Barlow packings such as FCC structure are the densest
unary sphere packings. Despite the simpleness ofthe problem, it is also too diﬃcult to identificate
the densest binary sphere packings (DBSPs), however, the development of computers has been enabling us to explore the DBSPs by computer simulations. If we only explore the periodic dense packings of spheres of n diﬀerent sizes by computer, thepacking fraction at given sphere-composition ratio
is maximized by filling the space with not morethan n kinds of periodic packings. In 2012, Hopkins et al. explored the DBSPs under the restriction that the number of spheres in the unit cellwith periodic boundary condition is not more than
12 and constructed the phase diagram for the firsttime. They also showed that in some radius andcomposition ratios a considerable number of thestructures have competitive packing fractions, so it
is necessary to optimize the packing structure precisely with small distortions.
We developed the structural optimization algorithm based on the steepest-decent method that iseasy to implement. The computational cost is small
and we showed that it is as precise as the TorquatoJiao algorithm. Our algorithm, which produces initial structures as layer-by-layer stacking structures,completely revisit the previous studies with small
corrections, and we successfully found the 12 putative DBSPs since the maximum number of spheresin unit cell is set to be double. As a result, a total of 28 putative DBSPs are known at the presenttime.
In ternary systems, the DBSPs are the candidates for the phase separations as well as the FCC
packings of small, medium, and large spheres. Following our studies, we constructed the phase diagrams for the first time since previous studies on
the dense ternary sphere packings (DTSPs) do not

(a)

(b)

Figure 1: (a) The (13-2-1) structure. (b) The (121-2-1) structure.
(a)

(b)

Figure 2: (a) The (13-3-1) structure. (b) The (121-3-1) structure.
construct the phase diagrams. As a result, we totally discovered the 59 putative DTSPs [1]. Oneof the well-ordered DTSPs is the (13-2-1) structure shown in Fig. 1(a). The structure is the
DTSP at several radius ratios such as 0.44 : 0.64
: 1.00, at which small and medium spheres are toolarge to be placed in the tetrahedral and octahedral sites if large spheres comprising the fcc structure contact with each other. The clathrate network of small spheres correspond to that of boron
in UB12 , and the (13-2-1) structure has one additional small sphere placed in the cuboctahedron ofsmall spheres. A medium sphere is placed in thetetrahedral site as CaF2 .
The clathrate network has structural similarity
with LaH10 and YH6 . The crystal structures of72

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Table 1: The list of substitution atoms for spheres, where the symbol E corresponds to empty.
sphere size
elements
smallH
semi-small E, H, Li, Be, Na, Mg, Al, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn
mediumFrom Li to Hg (except for B to Ne, Si to Ar, Br, Kr, Xe, Ce to Lu, Pt)
large
From K to Hg (except for Br, Kr, Xe, Ce to Lu, Pt)

the binary hydrides correspond to the DBSPs of the
XY10 and (6-1) structures, respectively. Therefore,
we can expect that the (13-2-1) structure also correspond the hydrides under high pressure. However,crystal structures tend to prefer the coordination
polyhedron [72]. Therefore, we replace the small
sphere at the center of the cuboctahedron withmetallic atoms so that the metallic atoms should
be coordinated by 12 small spheres comprising a
cuboctahedron. We call the quaternary structure
shown in Fig. 1(b) as the (12-1-2-1) structure. This
structure also has the F m3̄m symmetry. We also
focus on the (13-3-1) structure shown in Fig. 2(a).
It is the DTSP at several radius ratios such as
0.30 : 0.40 : 1.00 and can be derived by substituting an atom at the vertex of the unit cell of
the perovskite structure with a cuboctahedron of
13 small spheres. To produce the structural prototype of hydrides, we replace the small sphere at thecenter of the cuboctahedron with metallic atoms asshown in Fig. 2(b), and we call it as the (12-1-3-1)
structure.
Table 1 shows the list of substitution atoms forspheres in the (12-1-2-1) and (12-1-3-1) structures.
Small spheres are substituted with only hydrogenatoms. The number of chemical compositions foreach structural prototype is 36652. To screen out
unstable candidates, we execute the structural optimizations and molecular dynamic simulations withstatic/dynamic crystal lattices. We use the standard enthalpies of formations (SEFs) to estimate
the structural stabilities. We calculate the phonon
dispersions for some of hydrides which have lower
SEFs and no spin polarization.
As a result, we confirm that at least 23 hydrides
are dynamically stable [2]. We found that many
hydrides prefer the (12-1-2-1)V structure shown in
Fig. 3 to the (12-1-2-1) structure. It it worth mentioning that the (12-1-2-1)V structure is isotypic to
the P m3̄ structure of ScYH6 (cP8). Importantly,
the previous study indicated that cP8-ScYH6 may
possess the lowest enthalpy in the pressure rangeof 50150 GPa. Since several quaternary hydrides

(a)

(b)

Figure 3: The (12-1-2-1)V structure.
of which we confirm the dynamic stabilities are
the superstructures of cP8-ScYH6 , we can expectthat they may also have the lowest enthalpies under higher pressures. In fact, the SEFs under 10
GPa of the four kinds of the decomposition paths
from H12 ScY2 Ca:
ScH2 + 2YH2 + CaH2 + 2H2 ,(1)

ScH2 + 2YH3 + CaH2 + H2 ,(2)

ScH3 + 2YH2 + CaH2 + 1.5H2 ,(3)

ScH3 + 2YH3 + CaH2 + 0.5H2 ,(4)are calculated, and we found that H12 ScY2 Ca areconsiderably stable since it has almost the sameenthalpy as the decomposition of Eqs. (1) and(3) while it is less stable than the decompositionsof Eqs. (2) and (4) by about 1.2 eV/f.u., corresponding to the thermodynamic instability of 78.6
meV/atom compared to the case of Eq. (4).
Finally, we calculate the superconducting transition temperatures Tc by density functional theory
for superconductors, are found to be 5.7 and 6.7
K for the selected two hydrides H12 ScY2 Ca andH12 ScY2 Sr, respectively [2].

References[1] R. Koshoji and T. Ozaki: J. Phys. Commun.
6, 075002 (2022).
[2] R. Koshoji, M. Fukuda, M. Kawamura, T.
Ozaki: Phys. Rev. Materials. 6, 114802(2022).

73

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Control of magnetic anisotropy by strain andelectric polarization at interfaces
Yoshihiro GOHDA
Department of Materials Science and Engineering, Tokyo Institute of Technology
J1-3, Nagatsuta-cho 4259, Midori-ku, Yokohama 226-8502, Japaning to the ↑↓ spin-ﬂip term, i.e., couplings
between up-spin unperturbed states anddown-spin virtual states, in Fig. 1(b). In
contrast, the MCA energy modulation in
Fe3 Si was found to be attributed to neither
Bruno nor quadruple terms.
In addition, phase equilibria in permanent magnets were discussed through ﬁrstprinciples thermodynamics [3, 4].

E MCA (meV/atom)

In this project, we performed ﬁrstprinciples calculations of ferromagnetic
Heusler compounds to clarify the microscopic origin behind the strain-mediatedmodulation of the MCA energy EMCA . In
Co2 FeSi, site-projected EMCA of both Co
and Fe decrease with an isotropic expansion of the in-plane lattice as shown in
Fig. 1(a). This can be compared witha formulation connectable to X-ray magnetic circular dichroism observations via thequadrupole moment of the spin-density distribution (quadrupole term) and changes inorbital magnetic moments (Bruno term) [1].
As is clear from the ﬁgure, the quadrupoleterm is dominant for Co2 FeSi correspond-

0.10
0.08
0.06
0.04
0.02
0
−0.02
Bruno term
−0.04
−0.06 quadrupole term
EMCA
−0.08
−0.10
0.10
0.08
0.06
0.04
0.02
0
−0.02
Bruno term
−0.04
−0.06 quadrupole term
EMCA
−0.08
−0.10
−3 −2 −1
0

(b)

Co

E MCA (meV/atom)

E MCA (meV/atom)

(a)

Fe

1

Δa||/a0 (%)

2

E MCA (meV/atom)

Magnetocrystalline-anisotropy (MCA)
energy is of importance in controllingthe direction of magnetic moments. The
dependence of the magnetic anisotropy
on lattice constants can be utilized formagnetoelectric couplings via domain modulations of ferroelectric materials under
ferromagnetic ones. Such interface multiferroicity is promising for the reduction ofenergy consumptions in magnetic devices
avoiding the Joule heating by the electric
current.
The magnetoelectric eﬀect of
Co2 FeSi was found to be much larger thanthat of Fe3 Si [2], albeit its origin is yet to
be clariﬁed.

3

0.12

Co

0.08
0.04
0
−0.04
−0.08

E MCA

↑↑

E ↑↓
MCA

↓↓

E MCA

↓↑

E MCA

−0.12

0.12

Fe

0.08
0.04
0
−0.04
−0.08
−0.12
−3

E MCA

↑↑

E ↑↓
MCA

↓↓
E MCA

E MCA

−2

−1

↓↑

0

1

2

3

Δa||/a0 (%)

Figure 1: Site-resolved EMCA of Co2 FeSi as afunction of the distortion ratio ∆a|| /a0 : (a) total EMCA and contributions from the Bruno and↑↑↓↓↑↓quadrupole terms; (b) EMCA
, EMCA, EMCA,↓↑and EMCA[1] J. Stöhr, J. Magn. Magn. Mater. 200, 470(1999).
[2] S. Fujii, Y. Gohda, et al., NPG Asia Mater.
14, 43 (2022).
[3] S. Tsuna and Y. Gohda, J. Appl. Phys. 133,
115103 (2023).
[4] S. Nishino and Y. Gohda, Jpn. J. Appl.
Phys. 62, 030902 (2023).

74

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Discovery of Super-Composite Electrical Insulating Materials
Based on Multiscale Physics and Deep Learning of Polymers
Hajime SHIMAKAWA, Haruto SUZUKI, Chihiro TATEYAME, Yuki MORI,
Akiko KUMADA, and Masahiro SATO
Department of Electrical Engineering & Information Systems
The University of Tokyo, Hongo, Bunkyo-ku, Tokyo 113-8656
electrical

Materials Informatics (MI), a data-driven

conductivity

of

the

compositematerial design method, is considered effective

materials in PoLyInfo [2] were used. The

for designing polymer materials that require athermal and electrical conductivities were

vast amount of manufacturing information.

learned by adding polymer information, filler

However,

information

information, volume fraction, measurement

including fillers and measurement conditions

temperature, sample thickness, and process

have a great influence on material properties,

conditions,the methods for extracting them as features

improved with each additional feature. The

have not been sufficiently investigated, and theprediction accuracy increased significantly

use of MI is currently limited [1]. In this study,

when filler information was added for thermal

we investigated electrical and thermal feature

conductivity, and when process information

extraction for polymers using multi-scale

was added for electrical conductivity. Whilephysics based on first-principles and molecularfiller volume fraction had a significant effect on

dynamics calculations. We also used machine

prediction of thermal conductivity, it was notlearning to predict the electrical and thermal

sufficient

properties of composite polymer materials.

conductivity, indicating that input of process

Furthermore, the importance of each material

conditions is necessary in predicting electricalinformation was examined based on feature

conductivity. Our results show a new potential

engineering.

of materials informatics for the prediction of

althoughsamplesuccessfully

developed

for

the

prediction

predictionofaccuracyelectricalcomplex polymer properties.

In multiscale electrical feature computation,weandcomputational

methods of ionic charge mobility in polymer

References

matrix and hole trapping barrier between[1] L. Chen, et al.: Mater. Sci. Eng. R Rep.,

polymer matrix and nano-filler. In machine

144, 100595 (2021)

learning, experimental results of thermal and[2] https://polymer.nims.go.jp/en/

75

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First-principles study of photoresponsive functional materials
Shinji TSUNEYUKI
Department of Physics, The University of Tokyo
Hongo, Bunkyo-ku, Tokyo 113-0033
The technology to connect and control

We also find that the four-phonon scattering

semiconductor or magnetic devices with light isprocess contributes as much to the phonon

attracting attention as an essential technology inlinewidths of some phonon modes as the third-

the era of 5G and beyond 5G. The development

order anharmonic term. This method is versatileof materials and devices necessary for thisand relatively computationally inexpensive, andpurpose is required. This research aims towill be useful for future development ofdevelop and demonstrate a first-principles

dielectric materials.

simulation method for the optical responsivityof materials, which will serve as a basis forsuch research and development.
This academic year, we developed a firstprinciples method to calculate the dielectric
properties

ofcrystalswithsignificant

anharmonic effect in phonon in the sub-THz to
THz range and applied the technique to rutile
TiO2 [1].
The method is based on Cowley’s latticeFig.1 The imaginary part of the dielectric

dynamics theory on dielectric properties [2].

function exx. The results of the present method

We calculate the Born effective charges of ionsand a perturbative approach are show in a solidfrom the Berry phase and employ the modified

line and a dashed line, respectively (from Ref.

self-consistent phonon approach to calculate[1]).

phonon frequencies and linewidths, including

References

third and fourth-order anharmonicity.
We find that the optical phonon frequencies[1] T. Amano, T. Yamazaki, R. Akashi, T.

and line widths at the gamma point better agree

Tadano and S. Tsuneyuki, Phys. Rev. B, 107,with experimental measurements than those

094305 (2023).

obtained by the perturbative approach (Fig.1).

[2] R. Cowley, Adv. Phy. 12, 421 (1963).

76

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Analyses on electronic and magnetic structure and parallelization development in quasi-particle self-consistent GW code
Tatsuki ODA1,2, Kazuki MURANAKA2, Chandro PARDEDE2, Jakub LUTSINEC2,
Ko HYODO2, Masao OBATA1,2
1

Institute of Science and Engineering, Kanazawa University, Kanazawa, Ishikawa 920-1192
2

Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa,
Ishikawa, 920-1192
We have studied electronic structure infunctional approach of generalized gradient

several systems of magnetic material. In one ofapproximation (GGA). From the analysis of

the investigations, we found new foundations ofgeneralized susceptibility in the cubic phase, themagnetic shape memory alloy on the electronic

instabilities responsible for the modulated

instability to modulated phases using the quasi-

structures of 10M, 14M, and 6M were found toparticles self-consistent GW method (QSGW) in

be an intrinsic property in the electronic states

which the electron-correlation of random phase[2]. These states may stabilize the modulated

approximation is explicitly included in the self-

one, accompanied by tetragonal local JT

energy part of one particle energy level. Thisdistortions. Their property of Fermi surface

method was used in the software ecalj [1] which

nesting sensitively depends on a subtle change

has been developed by the researcher T. Kotani.

in the magnetic moment, corresponding to the

Our group has developed its highly parallelized

experimental fact that the modulated structure

code, achieving a one-order faster using theappears depending on temperature and thecomputer facilitated in ISSP.

We have also

composition of the magnetic element. We also

developed the density functional theory (DFT)found that there is another secondary peak in thecode optimized for GPU architecture.

generalized susceptibility.

(A) Application to Ni2MnGa by QSGW method

The results of QSGW show a lot of improved

According to the QSGW approach, the Ni eg

features in the electronic structure. The Ni 3d eg

orbitals in the cubic (austenite) phase, which

component, appearing just at the Fermi level,lead to martensite phase transition by band Jahn-

can be definitive theoretical evidence for the

Teller effect, were found to locate just on theexistence

Fermi level. This implies a clear definitive origin

temperatures. Compared with the GGA +U orof band Jahn-Teller (JT) effect in comparison

GGA results, the eigenvalue levels of thewith the results obtained by the density

minority spin state shift to higher energies,

77

of

phase

instability

at

low

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

indicating an increase in exchange splitting and,thus, an increasing total magnetic moment. Theenergy level shift in the minority spin state issmall but makes a drastic change in thegeneralized

susceptibility. Thechangeisquantitative, but the meaning of its result
provides a qualitative conclusion in themartensite phase of 10M or 14M appearing at
low temperatures. Such an instability indicatedfrom the investigation of susceptibility is foundFig. 1: Time ratio of GPU vs. CPU.

to be much more sensitive to the magnetic

We combined the use of OpenACC program-

moment, implying a close relationship with theming and CUDA library that provides thecomposition of the magnetic element and theroutines of FFT and MMM. The OpenACC canexternal magnetic field.

manage data transfer from host to device and(B) Optimization of DFT code for GPU

vice versa easily and parallelize some kinds of

The use of GPU parallelization has shown acalculations

great promise in accelerating DFT calculations

withsimpledirective.

We

performed DFT calculations on the different

and reducing the computational cost. The results

system size; modulated magnetic shape memoryobtained from our GPU-accelerated DFT codealloy Ni2MnGa (NMG); (NMG)10(40 atoms),

demonstrate its potential to accelerate electronic(NMG)20(80structure calculations, making it a promisingatoms),(NMG)14(56atoms),(NMG)28(112 atoms). Our code gained speed-up

tool for studying large-scale systems in materials

65-195-fold depending on its system size.

science, physics, and chemistry.

However, we faced memory limitation due to

Several of the subprogram in the DFT code

memory capacity on the GPU. As the larger

need a lot of memory data for accomplishingsystem we calculate, the large memory capacity

their operations. In a plane-wave-basis code, thewe need to perform the calculation.

routines of fast Fourier transformation (FFT)

References

and matrix-matrix multiplication (MMM) are[1] T. Kotani, J. Phys. Soc. Jpn. 83, 094711

essential in the total computational cost. The(2014); ecalj: https://github.com/tkotani/ecalj/.

problem has been a large amount of data transfer[2] M. Obata, T. Kotani, and T. Oda, Phys. Rev.

between GPU and CPU. Such problem may be

Mater. 7, 024413 (2023).

resolved in our plane wave code with a currently
available GPU equipped at ISSP computer.

78

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Systematic elucidation of structure and electronic state of πelectron-proton coupled molecular conductors under pressure
Shun DEKURA†
Institute for Solid State Physics, The University of Tokyo,
Kashiwanoha 5-1-5, Kashiwa, Chiba 277-8581
κ-H3(Cat-EDT-TTF)2 (= H-TTF) and its

below 2.0 GPa. The results are consistent withanalogues, which have been recently developed

the experimentally observed behavior, andin our group, show unique π-electron–proton

suggests that isotropy of the applied pressure, in

coupled physical properties owing to theaddition to the band width increase, is key tostrongly correlated electrons and dynamics of

cause metallization in H-ST. However, structural

protons in the H-bonds: such as quantum spin

optimization of H-TTF under hydrostatic

liquid / quantum paraelectric states or chargepressure did not result in proton localization

ordering transition coupled to the dynamics ofunlike the experimental observation, which

1–4)

protons.

Although H-TTF shows charge

indicates the importance of quantum nature ofordering

withprotons to fully understand the behavior of H-

protonlocalizationunderhydrostatic pressure, it was very recently foundTTF analogues under pressure.

that S/Se substituted H-TTF analogue, H-ST,

Referencesshows1) H. Mori, S. Yokomori, S. Dekura, and A.

metallization

andsuperconducting

transition under hydrostatic pressure, and shows
metal–insulator

uniaxial

2) A. Ueda, S. Yamada, T. Isono, H. Kamo, A.

pressure. To understand the π-electron–proton

Nakao, R. Kumai, H. Nakao, Y. Murakami,

coupled physical properties of H-ST underK. Yamamoto, Y. Nishio, and H. Mori, J. Am.

physical pressure, in this study, the author

Chem. Soc. 136, 12184 (2014).

performed

transition

under

Ueda, Chem. Commun. 58, 5668 (2022).

first-principles

structural

3) A. Ueda, K. Kishimoto, T. Isono, S. Yamada,

optimization of H-ST under hydrostatic and

H. Kamo, K. Kobayashi, R. Kumai, Y.

uniaxial pressure, using OpenMX software (Ver.

Murakami, J. Gouchi, Y. Uwatoko, Y. Nishio,

3.9) on the ISSP Supercomputer System B

and H. Mori, RSC Adv. 9, 18353 (2019).

(ohtaka), using GGA-PBE exchange-correlation4) M. Shimozawa, K. Hashimoto, A. Ueda, Y.

functional.

Suzuki, K. Sugii, S. Yamada, Y. Imai, R.

Structural optimizations were performed by

Kobayashi, K. Itoh, S. Iguchi, M. Naka, S.

minimizing the enthalpy under hydrostatic or

Ishihara, H. Mori, T. Sasaki, and M.

uniaxial pressures up to 2.0 GPa. The optimized

Yamashita, Nat. Commun. 8, 1821 (2017).

structures under hydrostatic pressures did not†show proton localization up to 2.0 GPa. On thefor Advanced Materials (IMRAM), Tohoku University,

Current address: Institute of Multidisciplinary Research

2-1-1 Katahira, Aoba-ku, Sendai 980-8577

other hand, the optimized structures under
uniaxial pressures showed proton localization

79

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Elucidation of the mechanism of fast proton conduction
based on proton tautomerism in molecular crystals
by using first-principles calculations
Shun DEKURA†
Institute for Solid State Physics, The University of Tokyo,
Kashiwanoha 5-1-5, Kashiwa, Chiba 277-8581
The author has been developing anhydrous

formation with transient proton transfer tosuperprotonic conductors based on single-

phosphatecrystalline molecular solids, and elucidatingcorresponding Ea was ca. 1.2 eV, consistent with

their peculiar conduction mechanism involving

the experimentally observed one. The result

1–4)

molecular internal degrees of freedom.

In thisintheadjacent

layer.

The

strongly supports the low-barrier anhydrous

study, we elucidated the mechanism of

protonconduction

realizedbyproton−3anhydrous superprotonic conduction (σ > 10

tautomerism. However, only one step of wholeS/cm) with the low activation energy (Ea) of ca.

proton conduction process has been elucidated

1.0 eV based on proton tautomerism in 1,2,3-

in this research; the other processes are now

triazolium phosphate. To evaluate the activation

under consideration.

energies

References

accompanied

with

the

protonconduction in the crystal structure, first-

1) Y. Sunairi, S. Dekura, A. Ueda, T. Ida, M.

principles NEB calculations were performed

Mizuno, and H. Mori, J. Phys. Soc. Jpn. 89,

using OpenMX software (Ver. 3.9) on the ISSP

051008 (2020).

Supercomputer System B (ohtaka), using a2) S. Dekura, Y. Sunairi, K. Okamoto, F. Takeiri,

generalized gradient approximation (GGA)

G. Kobayashi, Y. Hori, Y. Shigeta, and H. Mori,

proposed by Perdew, Burke, and Ernzerhof toSolid State Ionics 372, 115775 (2021).

the exchange-correlation functional.

3) Y. Hori, S. Dekura, Y. Sunairi, T. Ida, M.

At first, the experimentally obtained X-ray

Mizuno, H. Mori, and Y. Shigeta, J. Phys. Chem.

structure was optimized fixing the unit-cell

Lett. 12, 5390 (2021).

geometry for the initial state, where all the four4) S. Dekura, M. Mizuno, and H. Mori, Angew.

1,2,3-triazolium in the unit cell are 1H,3H-

Chem. Int. Ed. 61, e202212872 (2022).

tautomer. Then, the structure with one 2H,3H-†

tautomer out of four 1,2,3-triazolium in the unit

for Advanced Materials (IMRAM), Tohoku University,

cell was optimized as final state. The NEB

2-1-1 Katahira, Aoba-ku, Sendai 980-8577

Current address: Institute of Multidisciplinary Researchcalculation for this step suggested that thistautomerization occurs via hydrogen-bond80Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First-principles calculations of electron correlation parameters to
elucidate π-electron–proton coupled physical properties of theunconventional molecular conductors Cat-TTF analogues
Shun DEKURA†
Institute for Solid State Physics, The University of Tokyo,
Kashiwanoha 5-1-5, Kashiwa, Chiba 277-8581
κ-H3(Cat-EDT-TTF)2 (= H-TTF) and its

from the band crossing the Fermi level using

analogues, which have been recently developed

RESPACK software. Then, U and V for eachin our group, show unique π-electron–proton

analogue were evaluated by the screened direct

coupled physical properties owing to theintegrals between two MLWFs in the crystals.

strongly correlated electrons and dynamics ofBased on the comparison of the obtained1)

protons in the H-bonds. In particular, a charge-

electronicordering (CO) phase transition coupled to thetemperature (TCO), W and V were found to belocalization of D in H-bonds occurs in H/Dcorrelated with TCO. Considering that the CO

2)

and

CO

transitionCOtransition occurs between CO and Mott

transition temperature varies upon the S/Se

insulating states, the author concluded that V

substitution in the π-electron framework and theplays important role to determine TCO althoughisotopesubstituted

analogue.

3,4)

application of physical pressure;The

parameters

however, thepreviously H(D) in the H-bonds governs andmechanism had not been revealed. In this study,

triggers the CO transition.

to investigate the impact of electron correlation

Referenceson the π-electron–proton coupled CO transition,

1) H. Mori, S. Yokomori, S. Dekura, and A.

the author evaluated the bandwidth W, on-site

Ueda, Chem. Commun. 58, 5668 (2022).

Coulomb repulsion U, and inter-site Coulomb

2) A. Ueda, S. Yamada, T. Isono, H. Kamo, A.

repulsion V of the H-TTF analogues by first-

Nakao, R. Kumai, H. Nakao, Y. Murakami,

principles DFT calculations based on the

K. Yamamoto, Y. Nishio, and H. Mori, J. Am.

experimental X-ray structures.

Chem. Soc. 136, 12184 (2014).

All the calculations were performed on the3) A. Ueda, A. Hatakeyama, M. Enomoto, R.

ISSP Supercomputer System B (ohtaka). The

Kumai, Y. Murakami, and H. Mori, Chem.

electronic band structures were calculated by

Eur. J. 21, 15020 (2015).

using Quantum Espresso (version 6.8), where

4) A. Ueda, K. Kishimoto, T. Isono, S. Yamada,the half-filled HOMO–HOMO antibonding

H. Kamo, K. Kobayashi, R. Kumai, Y.

band crosses the Fermi level. The maximally

Murakami, J. Gouchi, Y. Uwatoko, Y. Nishio,

localized Wannier functions (MLWFs) with theshapesconsistent

withand H. Mori, RSC Adv. 9, 18353 (2019).

HOMO–HOMO

Current address: Institute of Multidisciplinary Research†

antibonding orbitals were successfully obtainedfor Advanced Materials (IMRAM), Tohoku University,
2-1-1 Katahira, Aoba-ku, Sendai 980-8577

81

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Structural analysis on twist grain-boundaries
Kazutoshi INOUE1 , Mitsuhiro SAITO2 , and Yuichi IKUHARA1,2,∗
1Advanced Institute for Materials Research, Tohoku University, Sendai, Miyagi, 980-8577
2
Institute of Engineering Innovation, The University of Tokyo, Yayoi, Tokyo, 113-8656

Revealing the relationship between atomic
structure and macroscopic properties is one of
the most important issues in materials science.
Grain boundaries (GBs) are especially important for their complex atomic structures andcontribution to the properties. Historically,
atomic structures of tilt GBs have been discussed by structural unit model. However, it
is essential to consider the 3D atomic structure for twist GBs. The polyhedral unit model,
which is an updated version of the structural unit model, rigorously describes the 3D
GB atomic structure using a combination of
atomic polyhedra. In this study, atomic models for [001] twist GBs in metals with Σ ≤ 53
were built with a 3D periodic boundary condition. The translation models are systematically constructed by the 0.1 Å steps within the
Displacement-Complete-Shift lattice to screen
the stable atomic structures. Each model was
ﬁrstly calculated to obtain GB energy without relaxation to screen the translation states.
We applied the ﬁrst-principles DFT calculations with the plane-wave basis projectoraugmented wave method included in VASP
to screen and determine the stable atomic
structure by using cpu nodes in System B.
The generalized gradient approximation wasused for the exchange-correlation potentials in
the Perdew-Burke-Ernzerhof form, employing
an ultrasoft pseudopotential. The cutoﬀ energy was set 400 eV for all plane-wave basis
sets. The Brillouin-zone integrations were performed over a 4 × 4 × 1 k-point mesh generated
by the Monkhorst-Pack scheme. The GB energy is the interfacial excess free energy calcu-

lated by∆E =1
(Etotal − N σi ) ,2A

where Etotal is the total energy of the system
including GBs, σi is the energy of the unit cellof the bulk crystal, N is the number of unit
cells contained in the supercell, and A is thearea of the GB. The GB energies for a ﬁxed
intergrain separation are within 0.1 J/m2 , indicating the energy landscape is almost ﬂat.
Then, atomic positions as well as the supercell
volume of the most stable structure were relaxed such that all forces were < 1.0 × 10−5eV/Å, and the residual force on each relaxed
atom was 0.05 eV/Åunder the constant pressure of 0 Pa and constant temperature of 0 K.
Then, interstitial voids in each model are calculated by Voronoi analysis, placing the sphereof maximal radius at the Voronoi vertex. The
maximum diameter for each void center can becalculated within the hard sphere model andtheir maximum values vary from 0.30 to 0.50
in the unit of the lattice constant a0 . We showthat the GB energies and the maximum void
volumes have relatively high correlation, suggesting that the maximum void volume can beused to screen low energy translation states for
twist GBs [1].

References[1] K. Inoue, K. Kawahara, M. Saito, M.
Kotani, Y. Ikuhara: preprint (2022).

82

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First-Principles Lattice-Dynamics Calculations andThermal Expansion Behavior for NASICONs
Yasuhide MOCHIZUKI1, Takayuki NAGAI2, Suguru YOSHIDA3, Tsuyoshi KIMURA2
1

Department of Materials Science and Engineering,Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo
2

Department of Advanced Materials Science,

University of Tokyo, Kashiwa 277-8561 Chiba, Japan
3

Materials Research Institute, Pennsylvania State University, University Park,
Pennsylvania 16802, United States

Negative-thermal-expansion materials haveattracted considerable attention because they
contribute to sustain the lifespan of largely
expanding materials and also because of theirfascinating

andcounter-intuitive

thermalproperties. The well-known and representative
NTE materials are Si [1], ReO3 [2], ScF3 [3]ZrW2O8 [4] and KZr2P3O12 (NASICON) [5].
Since the reports of NTE in various materials in
the past few decades, it has been widely known
that the mechanism for NTE is the transverse
acoustic phonon modes, which have negative

Fig. 1: Calculated phonon bands for the R 3̅ c

Grüneisen parameters. However, the detailed

phases of (a) KZr2(PO4)3, (b) KGe2(PO4)3, andmechanisms for the NTE properties are

the R 3̅ phase of (c) KGe2(PO4)3. Calculated

uncovered ever since now. Here, we report ourthermal expansion coefficients of KZr2(PO4)3 andprogress for elucidating the microscopic origin

KGe2(PO4)3 are also shown.

of NTE in KZr2P3O12 and the relevant[7] as implemented in VASP [8]. The phononcompounds.

band

structures

werederived

from

the

were

calculated force constants using PHONOPY [9].

performed by using the projector augmented-

We present the calculation results of phonon

wave method [6], and the PBEsol functional̅ c phases of KZr2(PO4)3 (KZP)
bands for the R3

within the generalized gradient approximation

and KGe2(PO4)3 (KGP) as shown in Fig. 1(a, b).

Ourfirst-principles

calculations

83

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

We can see that KZP is dynamically stable,

representation Γ2+ triggers second-order Jahn-

whereas KGP is dynamically unstable. We

Teller effect which lowers the electrons energy.

found that the imaginary phonon mode in the

Further, because the Γ2+ phonon mode is crucial

̅ c phase of KGP is Γ2+ (A2g) phonon mode
R3

for realizing negative thermal expansion, the̅ phase, whichleading to dynamically stable R3

̅ phase)lowered symmetry structure of KGP (R3is shown in Fig. 1(c). In our previous report, we

was transformed into a positive thermalfound that the Γ2+ phonon mode (the rotational

expansion material.

modes of ZrO6 octahedra and PO4 tetrahedra) is

References

the most important for realizing negative[1] S. Biernacki and M. Scheffler, Phys. Rev.

thermal expansion in KZP [10] since the Γ2+

Lett. 63, 290 (1989).

phonon mode has the largest negative value of[2] T. Chatterji et al., Phys. Rev. B 78, 134105

Grüneisen parameter. By substituting d-block(2008).

element Zr with p-block element Ge, the Γ2+[3] B. K. Greve et al., J. Am. Chem. Soc. 132,

phonon mode became unstable because the15496 (2010).

rotational Γ2+ mode of GeO6 octahedra and PO4[4] C. Martinek and F. Hummel, J. Am. Ceram.

tetrahedra trigger second-order Jahn-Teller

Soc. 51, 227 (1968); T. A. Mary et al., Science

effect, which lowers the electrons energy [11].

272, 90 (1996).

Here, as shown in Fig. 1(d), we have calculated[5] W. Miller et al., J. Mater. Sci. 44, 5441

the thermal expansion coefficients for KZP(2009).

̅ c phase) and KGP (R 3
̅ phase) by using(R 3[6] P. E. Blöchl, Phys. Rev. B 50, 17953 (1994).

quasi-harmonic approximation. We found that[7] J. P. Perdew et al., Phys. Rev. Lett. 100,

̅ phase of KGP has positive thermal
the R 3136406 (2008).

expansion coefficients above room temperature[8] G. Kresse and J. Furthmüller, Phys. Rev. B

because the Γ2+ phonon mode was transformed

54, 11169 (1996); G. Kresse and D. Joubert,

̅ , the negative
into Γ1+ (Ag) phonon in R 3

ibid 59, 1758 (1999).

Grüneisen parameter value of which was[9] A. Togo and I. Tanaka, Scr. Mater. 108, 1

weakened.

(2015).

In

conclusion,

we

have

shown

that

[10] Y. Mochizuki, S. Bae, and H. Raebiger,

substituting Zr site with p-block element Ge

Activity Report of Supercomputer Center̅ c phase (NASICON structure)makes the R 3

Institute for Solid State Physics 2021.

dynamically unstable because the rotational[11] T. Nagai, Y. Mochizuki, S. Yoshida, and T.

phonon mode transforming as irreducible

Kimura, J. Am. Chem. Soc. 145, 8090 (2023).

84

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Prediction of CO Adsorption Energy on Single AtomCatalysts Embedded in TiO2 by Machine Learning
Kyoichi SAWABE
Graduate School of Engineering, Nagoya University, Chikusa, Nagoya 464-8603

on r-TiO2 . Applying the learning model obtained with the CO/r-TiO2 data to the caseof CO/a-TiO2 (anatase type TiO2 ), we obtained Figure 1b, in which the deviation ofdata points from the straight line of the prediction model appears as CO adsorption becomesstronger. Thus, the features of π back donation and structural relaxation for the CO adsorption can be extracted from the additional
data for a-TiO2 .
By eliminating the parameters that arehighly correlated with each other using a
Lasso regression, etc., a multiple linear regression (MR) model shown in Figure 1c
was obtained.
We used six parameters:
Eads (CH3 CN), Charge, melting point, the heatof melting, crystalline system, and RMO,
which is the distance between the embedded
metal and the horizontally neighboring oxygen. Although the melting point and the heatof melting do not appear to have any chemicalsigniﬁcance for CO adsorption, the above analysis indicated that they relate to the π backdonation and/or the structural relaxation.
(a)

(b)

(c)

Eads(CO) /eV

This year, we conducted a feature analysis for machine learning (ML) prediction of
CO adsorption energy on single-atom catalysts(SAC) using TiO2 substrates.
We used the VASP program for spin polarization DFT calculations. Slab units withn (n = 2 − 6) layers were used as models of
TiO2 surfaces. The electronic states of the
SAC were modulated by changing both the
thickness of the slab unit and the single metals
embedded in TiO2 .
Good features for ML would require parameters of a chemically meaningful nature suﬃcient to describe the adsorption. In the caseof CO adsorption on the SAC, σ donation, πback donation, and structural relaxation due
to the adsorption are involved in the adsorption strength. Therefore, ML should use features that can describe these properties. Furthermore, data with a suﬃcient variation ofthese features must be used for ML. The σ donation is related to the Lewis acidity of the adsorption site. The strength of CH3 CN adsorption is often used experimentally and theoretically in order to measure the strength of Lewis
acidity. Therefore, we calculated the strengthof CH3 CN adsorption on SACs. A linear regression of CO adsorption energy, Eads (CO),on r-TiO2 (rutile type TiO2 ) with respect toEads (CH3 CN) was obtained with good accuracy, as shown in Fig. 1a. This means that
σ donation is dominant for the CO adsorption on r-TiO2 . In other words, informationon π back donation and structural relaxation
was not available in the CO adsorption data

R2=0.93
RMSE=0.14

Eads(CH3CN) /eV

Eads(CH3CN) /eV

MR prediction /eV

Figure 1:
Linear regression model for
Eads (CO) on (a) r-TiO2 and (b) both r-TiO2
and a-TiO2 using Eads (CH3 CN). (c) Multiple
linear regression model.

85

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Quantum transport theory based on large-scale
first-principles electron transport calculations
NOBUHIKO KOBAYASHI
Department of Applied Physics, University of Tsukuba
1-1-1 Tennodai Tsukuba Ibaraki 305-8573

ture at the atomistic levels, and also to analyze
the transport properties of materials weakly
bonded by van der Waals interactions. The
electron and hole mobilities of the pentacenethin-film are calculated taking the eﬀect of intramolecular vibrations on both the partially
dressed polaron and the dynamic disorder into
consideration by using TD-WPD method. It
is demonstrate that electron mobility in highmobility organic semiconductors is indeed limited by polaron formation. [4]Analysis of transport properties from theatomistic level is an important issue from theviewpoint of materials science and technology. The purpose of this project is to analyze
and predict the transport properties of materials from first-principles. We have developed
the nonequilibrium Green’s function (NEGF)
method and the O(N) time dependent wavepacket diﬀusion (TD-WPD) method on thebasis of the density functional theory (DFT).
By using these methods, we have investigatedcharge, head and spin transport properties ofmaterials. [1]We have developed a Simulation code for
Atomistic Kohn-sham Equation (SAKE) for
the ab-initio electron transport calculation
based on the DFT and NEGF formalism. [2]We have applied the method to analysis ofthermoelectricity of Heusler compounds, chalcopyrite, and skutterudites. High performancesystems are designed based on analysis of electronic structures and thermoelectric properties.

Figure 1:
CuFeS2.

Organic semiconductors have attracted
much attention for their applications to flexible, printable, lightweight, and low-cost electronic devices. They are crystals that areassemblies of π-conjugated molecules weakly
bonded by van der Waals interactions, andelectron transport properties are significantly
influenced by electron-phonon interactions.
We developed the O(N) TD-WPD method forthe quantum transport calculation of huge systems of up to 100 million atoms a decadeago, and have been applied organic semiconductors. [3] It is possible to calculate theconductivity and the mobility of the systemwith micron-order lengths at room tempera-

Wavefunctions of chalcopyrite

References[1] N. Kobayashi, H. Ishii, K. Hirose, in 3D Local Structure and Functionality Design of Materials eds H. Daimon, Y. C. Sasaki (World
Scientific, 2018) p133.
[2] H.Takaki, N.Kobayashi, K.Hirose, J. Phys.:
Condens. Matter. 32 325901(2020)[3] H. Ishii, S. Obata, N. Niitsu, S. Watanabe, H.
Goto, K. Hirose, N. Kobayashi, T. Okamoto,and J. Takeya, Sci. Rep. 10, 2524 (2020)[4] H. Sato, S. A. Abd. Rahman, Y. Yamada,
H. Ishii and H. Yoshida Nat. Mater. 21, 910(2022).
86

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First-principles simulation of interfacial reactions
using density functional theory and classical liquid
theoryMinoru OTANICenter for Computational Sciences, University of Tsukuba
1-1-1, Tenno-dai, Tsukuba, Ibaraki 305-8577, JapanThe electrochemical properties at the interface between electrode and electrolyte havemuch attention to improving the electrochemical performances of rechargeable batteries, fuelcells, and electrochemical catalysts. Because
the electrochemical properties depend on thematerial properties constructing the electrochemical interface and environmental parameters such as the bias voltage, an exhaustive
study is indispensable to understand the details of physical phenomena at the electrochemical interfaces.

derestimates the dielectric constant of a solution with dipolar solvent molecules. The dielectrically consistent RISM (DRISM) is oneof the solutions to overcome the drawback of
the RISM theory. This year, we have combined DRISM with the ESM-RISM framework to increase the accuracy of electrochemical interface simulations and applied it to thePt(111)/aqueous water interface as a benchmark calculation [4].

To simulate the physical phenomena at theelectrochemical interface, we have continuously developed the ESM-RISM method [1]that combines density functional theory using the eﬀective screening medium technique(ESM) [2] and reference interaction site model(RISM) [3]. In this method, we treat the electrode surface and the reactants using the quantum mechanical theory and the solvation system using the classical solution theory. The
electrostatic and van der Waals-type potentials
describe the interaction between the quantum mechanical particles and classical solution. Because ESM-RISM is formulated under the grand-canonical ensemble, we can naturally address the formation of an electric double layer at the electrochemical interface using
the half-cell system.

First, to check the results of DRISM, we calculated the salt-concentration dependence of
the activity coeﬃcients for the bulk NaCl aqueous solution. The results of the conventional
RISM underestimate the activity coeﬃcientscompared to the experiment, but DRISM well
reproduces the experiment. Thus, DRISM improves the dielectric properties of the bulk solution. The results of electrochemical properties at the interface, such as the potentialof zero charges, standard hydrogen electrodepotential, double-layer capacitance, and thickness of the contact layer, are improved by
DRISM and in reasonable agreement with theprevious experiments and theories.

Although the modeling of the ESM-RISM
method is physically reasonable, there is a wellknown drawback of the RISM theory that un-

Further application results such as the theoretical analysis of oxide electrode degradations
and the electrochemical catalysts will be reported elsewhere.

87Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

References[1] S. Nishihara and M. Otani: Phys. Rev. B
96, 115429 (2017).
[2] M. Otani and O. Sugino: Phys. Rev. B
72, 115407 (2006).
[3] A. Kovalenko and F. Hirata: Chem. Phys.
Lett. 290, 237 (1998).
[4] S Hagiwara, S Nishihara, F Kuroda, andM Otani: Phys. Rev. Mater. 6, 093802(2022).

88

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Theory of tunneling currents at semiconductor pn junctions
in electric fields based on first-principles calculation
Takashi NAKAYAMA

Department of Physics, Chiba University
1-33 Yayoi, Inage, Chiba 263-8522
The tunnel field-effect transistor (TFET) is oneof promising candidates for future energy-saving
devices, due to its much steeper on-off switching
features compared to conventional MOS FETs.
Recently, we showed by the first-principles andone-dimensional wave-packet model calculations
that the cooping of Al and N atoms around theSi-pn junctions produces impurity states in Siband gap and such states make the resonance with
conduction-band states under the electric field of
pn-junction, which shortens the tunneling length
between p- and n-Si layers and markedly increases
tunneling currents, in agreement with experiments.
The purposes of this work are to study (i) thetunneling current at Al-N co-doped Si-pn junctions
by the three-dimensional more realistic calculationsand (ii) how such tunneling current changes by the
N-atom doping into the pn junctions made of direct
band-gap semiconductors such as InP.
We consider the (100)-directed 2x2 pn junctions
with 104 atom-layers of 5 typical semiconductors
like Si and InP, as shown in Fig.1. The N-atom
impurity is inserted in this junction. To simulate
electronic structures of pn junctions, we adopt theconventional sp3d5s* tight-binding model, whoseparameters are determined so as to reproduce
electronic structures by the first- principles
hybrid-DFT calculation. The calculation of thetunneling currents are performed using thenon-equilibrium Green’s function (NEGF)
method, where the semi-infinite electrodes areattached to the left and right sides of the present
junction in Fig.1.
Figures 2(a)-(b) show calculated tunnelingcurrents as a function of the electric field, for Si, Ge,
and GaP, and for InP and GaAs, respectively.

Fig.1. Schematic view of Al-N doped (100) Si-pnjunction employed in this work.

Fig.2. Calculated tunneling current as a functionof electric field in pn junctions made of (a)Al-N-doped Si and Ge and N-doped GaP, and (b)N-doped GaAs and InP. The results for nondoped cases are also shown by dashed lines.

Here, both the currents in the cases with andwithout Al-N/N impurity atoms are shown. It isclearly seen that the Al-N/N-atom doping
markedly increases the current by about 102-107
times for the former indirect band-gap systems,
while induces a slight current change for the latter

89

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Fig.3. Calculated transmission spectra acrosspn junctions with and without Al-N/N impurity
atoms by red and black lines, respectively, as afunction of the energy in the tunneling energy
window, for (a) Si and (b) InP.

Fig.4. Schematic pictures to explain the effectsof Al-N/N-atom doping on tunneling currents
at pn junctions made of (a) indirect band-gap
semiconductors like Si, Ge, and GaP, and (b)direct band-gap ones like InP and GaP.

direct band-gap systems.
To clarify the origin of current enhancement,
we show in Fig.3(a) the transmission spectra ofelectrons for Si. It is seen that the transmission
becomes much larger when impurity atoms arepresent, and its spectra have an extremely large
peak around E= -0.66. By analyzing the local
density of states around the pn junction, as shownin Fig.4(a), we found that the electric field in pnjunctions produces the resonance between isolated
Al-N/N states in the band gap and conductionband states. These resonance states shorten thetunneling length and markedly enhances thetunneling current, which is in good agreement withexperiments.
With respect to direct band-gap systems, weshow in Fig.3(b) the transmission spectra for InP.
It is seen that the transmission spectra have flat
shape not depending on the energy without
impurities, while they show small modulation

around E= -0.4eV for the N-doped junctions. We
found that, since the N-impurity states are locatedin the conduction bands for the direct band-gap
systems as shown in Fig.4(b), the impurity doping
only modulates the bands of host semiconductors,
resulting in the slight change of tunneling currents.
All these calculations were performed using thexTAPP, VASP, and our original pspwf and NEGF
codes. In order to realize the calculations for thepresent pn-junction systems, because the system ismade of a large number of atoms (1000-2000
atoms), the advanced computing facility having
multi-task and higher-speed CPU (more than 1280
cores × 2.0G Hz), larger-size memory (around
256GB), and larger-size strange (more than 2 TB)
is indispensable. These conditions are realized onlyby the present ISSP supercomputing systems.

90

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Data-driven materials development using anharmonic phonon database
Masato OHNISHI
Department of Mechanical Engineering, The University of Tokyo, Japan
In recent years, Materials Informatics (MI)the previous fiscal year (April 2022 to Marchtechnology, which integrates informatics and2023), we focused on establishing an automatedmaterials science, has been rapidly developing.

workflow

However, there is still a lack of databases for

conductivity of the stable materials, thoseanharmonic phonon properties, which arewithout

crucialthermalfrequencies, in Phonondb1. We also generatedfunctional materials, such as thermal insulators,

thermal conductivity data for many materials

thermoelectric materials, and thermal interface

using the developed automation code.

for

the

development

of

for

calculating

negativethe(imaginary)

thermalphononmaterials. In addition, because mechanicalThe developed automation code, written inproperties are closely linked to phonon

Python, efficiently handles crystal structures andproperties, atomic vibrations, the availability ofcalls first-principles software (VASP2) andan anharmonic phonon property database would

phonon analysis software (ALAMODE3). The

be valuable also for the design and development

established workflow is illustrated in Figure 1.

of various structural materials.

Although multiple processes are involved in the

In this project, we have been developing anentire calculation, the most computationallyanharmonic phonon property database (APDB)

expensive one is calculating forces using thefor data-driven materials development. During

first-principles approach. As we aim to analyze

Fig. 1 Workflow for the automation calculation of anharmonic phonon properties

91

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

many materials, parallelizing the calculation formaterials in Phonondb in the fiscal year of 2023,

each material is unnecessary. Instead, the "bulk"

we will release the developed database withjob, which allows the submission of numerous

computational details. The released databasesimilar jobs as a single job, is optimal for ourshould allow easy accessibility to anharmonic

simulation. Using the "bulk" job, we can analyze

phonon properties, enabling accurate prediction

up to 144 materials simultaneously in ISSP

of material properties as well as material design

supercomputer. As a result, we have calculated

for target properties.

thermal conductivities for approximately 2,000

Referencesmaterials using the ISSP supercomputer as well

[1] Togo, http://phonondb.mtl.kyoto-u.ac.jp.

as HPCI supercomputers. Moreover, we have[2] G. Kresse and J. Hafner, Phys. Rev. B 47 ,shared the developed automation code with558 (1993); ibid. 49 , 14 251 (1994).

collaborators worldwide so that they can also

[3] T. Tadano, Y. Gohda, and S. Tsuneyuki, J.

work on data generation.

Phys.: Condens. Matter 26, 225402 (2014)

After finishing the calculation for the stable

92Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Impurity effects on TiO2 and X-ray emission spectra ofmolecules by all-electron GW calculations using TOMBO
Kaoru OHNO
Graduate School of Engineering, Yokohama National University79-5 Tokiwadai, Hodogaya-ku, Yokohama 240-8501
We found a new stable phase of anatase

RIXS spectra of a CH3OH molecule calculated

TiO2 codoped with C and N having a relatively

by our method together with the experimental

small band gap in our GW calculations [1]data [5]. The agreement is excellent without

using the all-electron mixed basis program,

introducing any empirical fitting parameter.

TOMBO, which uses both plane waves andnumerical atomic orbitals as basis functions [2].
Under intermediate oxygen pressure, the band
gap of TiO2 codoped with C and N was foundto be 2.28 eV. Therefore, this phase can be used
as a visible-light-response photocatalyst.
The initial state of X-ray emission spectroscopy (XES) and resonant inelastic X-raya deep core hole after the X-ray photoelectron

Fig. 1: Calculated [4] (above) and experimental[5] (below) RIXS spectra of a CH3OH molecule.

spectroscopy (XPS) and X-ray photoabsorption

References

spectroscopy (XAS), so that their calculations[1] T. Ishikawa, R. Sahara, K. Ohno, K. Ueda, & T.

offer a good example of extended quasiparticle

Narushima: Comp. Mat. Sci. 220 (2023) 112059.

theory (EQPT) [3] in many-body perturbation[2] S. Ono, Y. Noguchi, R. Sahara, Y. Kawazoe,

theory (MBPT). Moreover, to overcome the& K. Ohno, Comp. Phys. Comm. 189 (2015) 20.

basis set incompleteness problem, we adopt the[3] K. Ohno, S. Ono, & T. Isobe: J. Chem. Phys.

all-electron mixed basis program, TOMBO.

146 (2017) 084108.

Here we applied the one-shot GW + Bethe--[4] K. Ohno & T. Aoki: PCCP 24 (2022) 16586.

Salpeter equation (BSE) approach in MBPT to[5] A. Benkert, F. Meyer, D. Hauschild, M.

this problem on the basis of EQPT and analyzeBlum, W. Yang, R. G. Wilks, M. Bär, F. Reinert,

XES and RIXS energies for CH4, NH3, H2O,

C. Heske & L. Weinhardt,: J. Phys. Chem. A

and CH3OH molecules [4]. Figure 1 shows the120 (2016) 2260.

scattering (RIXS) is an excited eigenstate with93

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Development of a first-principles calculation software
for a many-body wave function theory
Masayuki OCHI
Forefront Research Center, Osaka University
1-1 Machikaneyama-cho, Toyonaka, Osaka 560-0043

We have developed a first-principles electronic structure calculation software using thetranscorrelated (TC) method [1, 2]. The TC
method is a many-body wave function theory,
where Hamiltonian is similarity-transformed
with the Jastrow correlation factor. By thistransformation, electron correlation eﬀects are
eﬃciently considered.
In particular, oneelectron orbitals in the Jastrow-Slater-typewave function can be optimized in the samemanner as the Hartree–Fock (HF) method, tosay, by solving a one-body self-consistent-field(SCF) equation. It is advantageous that thecomputational cost for this process is the sameorder as that for the HF method [3].
In this year, we have published our computational code TC++ on github [4] together
with its technical details [5], including efficient treatment of the three-body interactions included in the similarity-transformed
Hamiltonian and accurate correction terms
for the divergence of the eﬀective potentials in the reciprocal space. One can perform the TC calculation using TC++ by reading some output files dumped by DFT calculation using the Quantum-Espresso package [6]. TC++ supports the following functionalities: HF, TC, and biorthogonal TC(BITC) calculations, SCF and band calculations, solids and homogeneous electron gas,a plane-wave-basis set, and norm-conserving
pseudopotentials. At present, the Jastrow factor has a simple random-phase-approximationtype form, but TC++ will support more general

types of the Jastrow factor soon. Calculation is
MPI-parallelized for the k-point and band indices. Using supercomputers of ISSP, we have
performed several test calculations on several
kinds of solids, to check the applicability of
our code and verify the accuracy of calculated
quantities, e.g., a band structure and a totalenergy. As reported in our previous studies(e.g., [3]), calculated band gaps are more accurate than LDA and GGA, but the total energy
is at present less accurate. Now we have tested
the accuracy using a polynomial-type Jastrowfactor including a larger number of optimizable
parameters.

References[1] S. F. Boys and N. C. Handy, Proc. R. Soc.
London Ser. A 309, 209 (1969); ibid. 310,
43 (1969); ibid. 310, 63 (1969); ibid. 311,
309 (1969).
[2] N. C. Handy, Mol. Phys. 21, 817 (1971).
[3] M. Ochi, K. Sodeyama, R. Sakuma, and S.
Tsuneyuki, J. Chem. Phys. 136, 094108(2012).
[4] https://github.com/masaochi/TC[5] M. Ochi, Comput. Phys. Commun. 287,
108687 (2023).
[6] https://www.quantum-espresso.org/

94

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Development of Polymer Electret Materials for Energy Harvesting
Using Machine Learning
Yucheng ZHANG, Zeitan MAO, Kuniko SUZUKI, and Yuji SUZUKI
Department of Mechanical Engineering
The University of Tokyo, Hongo, Bunkyo-ku, Tokyo 113-8656
Electret is a dielectric material withgroup enrichment analysis, we successfully

quasi-permanent charges and can trap

proposed the electret CTX-A/APDEA withcharges stably for decades. Vibrational

high bipolar charging performance via

electret energy harvester (VEEH) can

combining CTX-A with N-(3-aminopropyl)

generate electricity efficiently from kinetic

diethanolamine [2].

energy via the electrostatic induction. The

This result encouraged us to move

VEEH with bipolar (both positively andfurther to search new amines in the open-

negatively charged) electret leads to four

source database PubChem, aimed attimes output power theoretically, thus

designing better bipolar polymer electret

specifying the importance of designing

based on CYTOP CTX-A after thehigh-performance bipolar polymer electrets.

dehydration reaction. It is also shown thatCYTOP (Cyclic Transparent Opticalthe ionization potentials (IPs) computed byPolymer, AGC Chemicals) is one of thethe density functional theory (DFT) withbest polymer electret materials as shown inpolarizable

Fig. 1. Recently, we utilized quantum

correction agree well with the trends of thechemical analysis for evaluating the charge

surface charge density and charge stability

trap of CYTOP electrets [1]. It is found thatmeasured by the wet experiments [3,4].

continuum

model

(PCM)

although the repeat-unit-number of CTX-A
is above 1000, the trapped charge islocalized at the amide bond, which isformed by dehydration reaction between
the carboxyl end group and the amine
introduced.
Recently, by employing the de novo

Fig. 1. Chemical structure of CYTOP series.

molecule generation and the functional

95

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

-3000
-2500
-2000
-1500
-1000

(-) CTX-A/APAEPPA(-) CTX-A/ABPE(-) CTX-A/BAPP(-) CTX-A/APDEA

-500
0
0

500

1000
1500
Elapsed Time [Hours]-2000time for negatively-charged with elapsed

3.5 kV, stored under room temperature.
-1500

To-1000
prepare the labeled data, 5522 amines(-) CTX-A/APAEPPA_115°C
were -500
initially sampled fromthe PubChem.
(-) CTX-A/ABPE_115°C(-) CTX-A/BAPP_115°C(-) CTX-A/APDEA_115°C

Thereafter, the virtual dehydration reaction
0

0
500
1000
1500
2000
was conducted
and PCM-DFT
computation

thermal curing temperature of the CYTOP

3500
3000

polymer.

2500
2000

Candidates are re-examined with PCM-DFT to get accurate solid-state IP. Three

1500

amines APAEPPA, ABPE,
and BAPP have(+) CTX-A/APAEPPA

1000(+) CTX-A/ABPE

CTX-A/BAPP
finally been selected.(+)
2 shows the
500(+)Figure
CTX-A/APDEA

0
surfacepotential
for thosefilms(15 µm in
0
500
1000
1500
2000

2000

c
Figure2. Surface potential decay versus
Negative Surface Potential at 115°C [V]Positive Surface Potential [V]

b

-3500

Elapsed Time [Hours]dPositive Surface Potential at 115°C [V]Negative Surface Potential [V]athickness) after charging to -3.5 kV. It isfound that the materials obtained in this2000

study are superior to our previous CTXA/APDEA in terms of charge stability [4].

1500

1000

All the quantum chemical calculations

are made with the software GAUSSIAN [5](+) CTX-A/ABPE_115°C
at the DFT levelwith the CAM-B3LYP(+) CTX-A/APAEPPA_115°C

500(+) CTX-A/BAPP_115°C(+) CTX-A/APDEA_115°C

functional and 6-31+G(d,p) basis set, while
0
0

500

1000

1500

2000

PCM is combinedforsolid-state analysis
Elapsed Time
[Hours]Elapsed Time [Hours]

was automatically made. Subsequently, 80%

due to the solvation effect correction.

of the data for solid-state IPs and theThis work was partially supported byinformation on molecules and bonds was

JST CREST Grant Number JPMJCR15Q3

employed to teach the deep learning modeland JPMJCR19Q1. The calculations are

named MEGNet [5]. Two sets of the data

performed with SGI ICE XA ISSP system(10 % each) were used as the validation/test

B with 1 node (24 CPUs).

sets. As a result, the mean absolute error(MAE) of solid-state IP is found to be as

References

small as 0.326 eV. The computational cost

[1] Kim S., et al., J. Phys. Chem. B, Vol.

with this approach is significantly lower

124, No. 46, pp. 10507-10513 (2020).

than PCM-DFT, enabling rapid screening[2] Zhang, Y., et al., Appl. Phy. Lett., 118,of huge databases to find candidates for

223904 (2021).

new high-performance bipolar polymer[3] Mao, Z., et al., IEEE Conf. Electr. Insul.

electrets.

Dielectr. Phenom., pp. 667-670 (2021).

We also made another MEGNet to[4] Mao, Z., et al., Adv. Mater., submitted.

predict the boiling point, because the[5] M. Frisch et al., “Gaussian 09, Rev. A.

amines should not be evaporated at the

1,” Gaussian, Inc. Wellingford, CT, (2009).

96

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Design and Understanding of Catalysts Realized by Theory
and Machine Learning
Hao LiAdvanced Institute for Materials Research (WPI-AIMR),
Tohoku University, Sendai 980-8577, Japan
1. Introduction

adsorbate, the surface state of an electrocatalyst

Catalyst design is essential for realizing a

is usually different from its pristine form atsustainable society. However, it is sometimes

operatingchallenging due to the complicated reaction

electrochemical surface state of a catalyst is

networks and surface phases under reaction

essential in determining the catalyst activity. In

conditions. To address this challenge, ab initio

this project, we analyzed the surface states ofcalculations,molecularandtypical transition metal X-ides (TMXs) andmicrokinetic

modeling

betteremerging M-N-C catalysts, and found that theirdynamics,
can

help

conditions.

Meanwhile,theunderstand the reaction and make predictions.

catalytic surfaces generally differ from their

2. Computational Methods

stoichiometric forms [1-2]. A typical example is

In

this

project,

DFT

calculations

were

shown in Fig. 1, where the surfaces of dual-

performed using VASP with the exchange–

atom M-N-C are usually pre-covered [1]. These

correlation energetics described by the Perdew,

show that we should probe the surface state

Burke, and Ernzerhof (PBE) or the revised PBE

before analyzing the electrocatalytic activity.

(RPBE) functional. The generalized gradientapproximation was employed. Ion–electroninteractions were described by the frameworkof projector-augmented plane-wave method.
The energy cutoff was set to at least 400 eV.
Spin-polarization was included when necessary.
Brillouin zone was sampled by the MonkhorstPack method. Transition states were located by
the climbing-image nudged elastic band.

Fig. 1: Examples of the calculated surface

3. Results and Discussion

Pourbaix

3.1. Surface state analysis

catalysts. (a-b) Optimized structures. (c-d)

Due to the equilibrium between water andSurface Pourbaix diagrams. Reprinted with97

diagramsofdual-atom

M-N-C

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

permission from Ref. [1].

ORR in alkaline media [6]. Based on ~7800

3.2. Catalysis theory development

experimental data, we derived the key role of
La and found that those materials with La could
possess good ORR performance. These showthat ML can not only provide prediction, butalso unravel the structure-performance relationof electrocatalysts.
4. Conclusion
Through this ISSP project, we performed
comprehensive analyses to understand somepromising electrocatalytic materials. We foundFig. 2: (a) The Er-NiFe-LDH considered forthat surface state is an essential factor insimulations. (b) Microkinetic modeling toelectrocatalysis that should not be dismissed

analyze OER activities of various sites at Er-

before the activity analyses of many catalysts.

NiFe-LDH. (c) Charge density differences. (d)Also, explainable ML can help identify the

Projected density of states. (e) Calculated spin-

critical factor of a material in electrocatalysis.

polarized d-band centers of different systems.

References

Reprinted with permission from Ref. [3].

[1] W. Yang, J. He, B. Zhou, L. Wei, Z. Gao*,

We also analyzed various catalysts and their

H. Li, Commun. Chem. 6, 6 (2023).

performances based on catalysis modeling. The[2] H. Liu, X. Jia, A. Cao, L. Wei, C.

analyzed catalyst systems include TMXs andD'agostino, H. Li, J. Chem. Phys. 158 (2023).

single-atom catalysts. The analyzed reactions[3] Y. Zhu, X. Wang, X. Zhu, Z. Wu, D. Zhao, F.

include oxygen evolution (OER) [3], oxygen

Wang, D. Sun, Y. Tang, H. Li, G. Fu, Small, 19,

reduction [4], and nitrogen reduction [5]. An

5 (2023).

example is shown in Fig. 2 [3], in which we[4] C. Liu, Z. Yu, F. She, J. Chen, F. Liu, J. Qu,

analyzed the OER activities of Er-doped NiFe

J. M. Cairney, C. Wu, K. Liu, W. Yang, H.

layered double-hydroxide (LDH). Besides, we

Zheng, Y. Chen, H. Li, L. Wei, Energy Environ.

also studied thermal catalysis, such as Hg 0

Sci. 16, 446 (2023).

oxidation.

[5] Y. Zhang, Z. Yu, F. She, L. Wei, Z. Zeng, H.

3.3. Machine learning (ML) analysis

Li, J. Colloid Inter. Sci. 640, 15 (2023).

In addition to DFT-based analysis, this project[6] R. Zhao, Z. Chen, Q. Li, X. Wang; Y. Tang,

performed explainable ML modeling to identify

G. Fu, H. Li, J-M. Lee, S. Huang, Chem. Catal.

the role of element La as the active center of

2, 3590 (2022).

98

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First principles study of the adsorption structure of heliceneon the Ag(111) surface
Yuji HAMAMOTO
Department of Precision Engineering, Graduate School of Engineering
Osaka University, Yamada-oka, Suita, Osaka 565-0871
Molecular chiral recognition has drawn

adsorption height and relax the whole system togrowing attention due to its importance indetermine the adsorption structure of 7TH on

biochemistry and pharmacology, and has beenAg(111). The adsorption structure shown in Fig.

studied using a model system composed of

1 indicates that 7TH physisorbs on Ag(111), i.e.,chiral molecules adsorbed on a solid surface.

the S atoms play a minor role in the molecular

The molecular adsorption structure provides aadsorption. This allows us to use classicalkey to the origin of chiral recognition, whereas

molecular

its detail is beyond the reach of experimental

computationally much cheaper than DFT, totechniques

investigate the formation of a 7TH chain on

such

as

scanning

tunneling

dynamics

(MD),

which

is

Ag(111). The results for the MD simulations

microscopy.

will be discussed in more detail elsewhere [3].

Here we resort to density functional theory(DFT) calculations to investigate the adsorption
structure of 7-thiaheterohelicene (7TH) on theAg(111) surface. Although 7TH forms a onedimensional chain on Ag(111), we focus on
single molecule adsorption to clarify theinteraction of the S atoms of 7TH with the Ag

Fig. 1: Adsorption structure of 7TH on Ag(111).

surface. The van der Waals interaction between

Top (a) and side (b) views are shown.

7TH and Ag(111) is considered by using thevan der Waals density functional (vdW-DF)

References

method [1] and the rev-vdW-DF2 functional [2].

[1] M. Dion, H. Rydberg, E. Schröder, D. C.

We first estimate the adsorption site andLangreth, and B. I. Lundqvist, Phys. Rev.
Lett. 92, 246401 (2004).

orientation of a 3TH molecule on Ag(111). The[2] I. Hamada, Phys. Rev. B 89, 121103(R)optimal site and orientation for 3TH are used tomake an initial guess on the local structure of(2014).

7TH near Ag(111). We then optimize the[3] C. Ye et al., in preparation.

99

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Global search for metastable structures of siliceneon the Ag(111) surface by Gaussian process regression
Yuji HAMAMOTO
Department of Precision Engineering, Graduate School of Engineering
Osaka University, Yamada-oka, Suita, Osaka 565-0871
Silicene, a silicon analog of graphene, hasresults predict at least seven phases for the

been intensively studied in the last decade,√13 × √13𝑅19.1° unit cell relative to Ag(111),

because of its peculiar phenomena, as well as

which are classified into type-II [Figs. 1(a)-its better compatibility with the silicon-based

1(c)] and type-I [Figs. 1(d)-(g)] phases based

electronics than graphene. Density functionalon the angle between the Ag[110] and Si[110]theorythat

directions. The type-I phases are characterized

freestanding silicene is characterized by theby one, two, or three protruded Si atoms, andalternately buckled honeycomb lattice, whereas

are energetically comparable with each other.

silicene grown on the Ag(111) surface exhibits

These behaviors are analogous to the so-calleda variety of nontrivial buckling patterns, which

T phase, whose structures is hard to identify

makes it difficult to theoretically predict stable

experimentally and still under discussion.

(DFT)

calculations

revealstructures of silicene on Ag(111). To overcome
the difficulty, we here adopt a structure searchmethod based on an evolutionary algorithm(EA) and the Gaussian process (GP) regression[2]. This method accelerates the structure
search by relaxing the structures generated with

Fig. 1: √13 × √13𝑅19.1° phases obtained with

EA, using the energy landscape predicted withthe structure search method. The bright (dark)

GP instead of that obtained by computationallycyan balls denote higher (lower) Si atoms.

expensive DFT calculations.
Our results demonstrate that the method not

References

only reproduces the well-known buckled[1] K. Takeda and K. Shiraishi, Phys. Rev. B

structures of silicene on Ag(111), but also

50, 14916 (1994).

predict new structures that have never been

[2] M. K. Bisbo and B. Hammer, Phys. Rev.

reported as far as we know. For example, ourLett. 124, 086102 (2020).

100

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

High-accuracy machine-learning interatomic potential and massively
large-scale molecular simulation for microscopic mechanisms of grainboundary-dislocation interactions
Tatsuya YOKOI
Department of Materials Physics
Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, 464-8601
In polycrystalline metals and alloys, grainthese lattice defects. In addition, classicalboundaries (GBs) exist and generally interact

empirical potentials are typically based onwith dislocations, thereby having great impacts

simple analytic functions and thus show limitedontransferability to various atomic environments

mechanicalproperties

thoughGBstrengthening. It is thus essential to understandfor lattice defects.

the relationship between GB structures, their

To tackle the above issue, this work constructedinteraction with dislocations and mechanicalan artificial-neural-network (ANN) interatomic

properties at various length scales, with the goal

potential and combined it with molecularofsimulations.

optimaldesignofpolycrystallineForapreliminary

analysis,

microstructure in metals and alloys.

aluminum was chosen as a model system [1,2].

For this purpose, previous theoretical studies

The ANN potential was used to predict favorable

performed large-scale molecular simulations

atomic structures and energetics for symmetric

basedinteratomic

tilt GBs. The predicted results were thenpotentials, in order to determine atomic structure

validated by performing density-functional-

and energetics at GBs. However, their adjustable

theory (DFT) calculations.

parameters are fitted only to bulk properties

As illustrated by Fig. 1, the ANN potential was

without consideration of atomic environments ofconstructed by following the architecture

GBs. This often leads to critical errors inreported by Behler and Parinello [3] and Behler

predicting atomic structures and energetics of[4], with two hidden layers each having 48 nodes.

onclassical

empirical

Fig. 1 Schematic illustration of an ANN potential.

101Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Two- and three-body symmetry functions wereenergies for symmetric tilt GBs with the [001]employed to encode the atomic environment ofand [1̅10] rotational axes. The predictedeach atom and to provide the input value for theatomic structures were used to perform DFT

ANN potential. In order to generate training

calculations and to calculate their GB energies

datasets, DFT calculations were performedin the DFT level. As a result, the ANN potential

using the projector augmented wave (PAW)

is found to accurately predict GB energies frommethod in the VASP [5,6]. The exchange-

low to high energy states for all the GBs

correlation energy was calculated using a

examined. In addition, the lowest-energy

revised version of the generalized gradientstructures predicted by the ANN potential wereapproximation parameterized by Perdew, Burke

the same as those for DFT calculations. Theseand Ernzerhof (GGA-PBEsol) [7].

results suggest that the ANN potential can be

In order to cover a wide range of atomic

used to predict the lowest-energy structures andenvironments, not only single crystals but also

their GB energies prior to DFT validation, which

lattice defects including point defects, surfaces

will greatly reduce computational cost for

and GBs were used as references for generating

searching for energetically favorable atomic

the training datasets. With those reference

structures for GBs.

structures, structural relaxation and molecular

In future research, the ANN potential will be

dynamic simulations were performed, andapplied to dislocations, in order to evaluate its

snapshots were extracted from the calculated

ability to predict core structures and formation

results. The total potential energy and atomic

energies for dislocations.

force were used as the training datasets. The
ANN potential was trained using an extended

References

Kalman filter and then combined with structural[1] T. Yokoi et al., submitted.

relaxation and molecular dynamics.

[2] T. Yokoi et al., prepared.

It was found that all training datasets exhibit[3] J. Behler, M. Parrinello, Phys. Rev. Lett.
98, 146401 (2007).

sufficient small errors, including the GBs in the[4] J. Behler, Int. J. Quantum Chem. 115,

training datasets. The mean absolute errors

1032-1050 (2015).

(MAEs) were evaluated to be 1.46 meV/atom[5] G. Kresse, D. Joubert, Phys. Rev. B 59,

and 17.1 meV/Å for the total energies andatomicforces,respectively,

which

1758-1775 (1999).

are[6] G. Kresse, J. Furthmüller, Comput. Mater.

comparable to previous studies. It is thus

Sci. 6, 15-50 (1996).

expected that the present ANN potential exhibits[7] J. P. Perdew et al., Phys. Rev. Lett. 100,an excellent accuracy to predict GBs.

136406 (2008).

Then structural relaxation calculations with the
ANN potential were performed to predict therelationship between atomic structures and GB

102

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Development of crystal structure prediction method
and material search
Tomoki YAMASHITATop Runner Incubation Center for Academia-Industry Fusion, Nagaoka University of Technology
1603-1 Kamitomioka-machi, Nagaoka, Niigata, 940-218
We have studied the structural stability of
SrO on MgO substrates since SrO with unknown crystal structure synthesized on MgOsubstrate has been reported [1]. There isan approximately 20 percent mismatch between the lattice parameters of SrO (5.16
Å) and MgO (4.22 Å), which could affectthe structural stability of SrO We performed
crystal structure prediction (CSP) simulationsfor Sr4 O4 using CrySPY [2]. Four hundred
structures of tetragonal Sr4 O4 were randomly
generated with the lattice constant of the
MgO(100) substrate. Only the lattice constant c was relaxed during structural optimization using Quantum ESPRESSO. Here,GGA is used for the exchange-correlation functional. The results of the search showed thatthe rocksalt-type structure was not the moststable under the constraint on the lattice constant a. The four-coordinated structures weremore stable than others. Alhough the predicted structure differs from that obtained experimentally, we found that the lattice constraints make the rocksalt structure unstable.
We have also searched for solid electrolytematerials that can be synthesized in the systems of Li-Mg-P2 O7 using CSP with CrySPY[2].
We perfromed randam search simulations for LiMg1.5 P2 O7 , Li2 MgP2 O7 , andLi3 Mg0.5 P2 O7 . In each composition, 1000 random structures were locally optimized using
the VASP code. The CSP simulations wereconducted using GGA-PBE, and PBEsol was
also used for the structures with low energy.

We obtained a structure with a hull distanceof 2 meV/atom for Li2 MgP2 O7 , and structureswith a hull distance of about 20 meV/atom for
LiMg1.5 P2 O7 and Li3 Mg0.5 P2 O7 . In addition
to the randam search simulations, DFT calculations were also performed to evaluate the energy of Li2 MgP2 O7 with the P2 O7 frameworkin the Li2.2 Zn0.8 P2 O7 (LZ) structure [3]. Froma vast number of combinations of cation sites
in the LZ structure, the top 10 structures withthe lowest Mardelung energy were adopted for
DFT calculations. The lowest energy of the LZ
structure was only a few meV higher than theenergy of the structure found in RS simulations(RS structure). The RS structure become unstable when the cationic site changes, which
increases the energy. On the other hand, theLZ structure have almost no change in energy
when the cation site was exchanged.

References[1] K. Komatsu, T. Shirai, A. Nakamura, A.
Kato, S. Ohshio, N. Nambu, I. Toda, H.
Muramatsu, H. Saitoh, Ceramics International 39, 7115 (2013).
[2] T. Yamashita, S. Kanehira, N. Sato, H.
Kino, H. Sawahata, T. Sato, F. Utsuno,
K. Tsuda, T. Miyake, and T. Oguchi, Sci.
Technol. Adv. Mater. Meth. 1 (2021) 87.
[3] A. E. Lapshin and M. A. Petrova, Glass
Physics and Chemistry, 36,75 (2010).

103

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Study of the Principle of Photothermal Conversion by Ab
Initio Calculations
Mizuho ONO1, Hiroki GONOME2
1

Graduate School of Engineering,Tohoku University, Aoba-ku, Sendai, Miyagi 980-0845
2

Graduate School of Science and Engineering,

Yamagata University, Jonan, Yonezawa, Yamagata 992-8510
In light-matter interactions, the electrons

W/cm2 and the central frequency is 3.10 eV.

and nuclei that composes matter interact with

Figure 2 shows the total energy excluding theeach other, but the physical processes are

kinetic energy of the lattice and the kinetic

extremely complex and fast. In simulations ofenergy of the lattice in the TDDFT-MD

matter that reproduce this situation, the Born-

simulation results for the silicon crystal. During

Oppenheimer

typically

the application of an electric field, the energy ofapplied and the motion of the electrons is

the system increases rapidly, while the kinetic

decoupled from that of the nucleus. On theenergy of the lattice remains almost unchanged.

other hand, it has been reported that non-In other words, the energy increase of thenegligible non-adiabatic effects between thesystem is dominated by the interaction between

electrons and the nucleus can act on molecules

the electromagnetic waves and the electrons.

moving under a strong external field [1]. The

The kinetic energy of the lattice then begins to

objective of this work is to evaluate the effectfluctuate after 300 fs due to electron-latticeof the external field on the energy of the systeminteractions. The numerical analysis therefore

by performing coupled calculations of theshows that the lattice system is hardly displaced

approximationiselectron and lattice (nuclear) systems without
applying the Born-Oppenheimer approximation.
The coupled calculations treat non-adiabatic
motion by applying Ehrenfest dynamics with
time-dependent density functional theory andclassical molecular dynamics (TDDFT-MD) forthe electron and lattice. The time waveform of
the incident electric field in the simulation isFig. 1: Incident electric field

shown in Figure 1. The light intensity is 1013

104

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Fig. 2: Total energy and kinetic energy of lattice.
directly by the applied electric field but
receives energy through the interaction with theelectron system.

References[1] C. Lian, S.B. Zhang and S. Meng, Phys.
Rev. B. 94, 184310 (2016).

105

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Development of a full GW electron-hole interaction kernel for
the Bethe-Salpeter equation
Yoshifumi NOGUCHI
Department of Applied Chemistry and Biochemical Engineering, Graduate School ofEngineering, Shizuoka University, Johoku 3-5-1, Hamamatsu, Shizuoka 432-8561, JapanIn 1995, Onida, et al., initiated the first-

gap of 28 typical organic molecules in Thiel’s

principles GW+Bethe-Salpeter simulations for aset [4] and revealed the contributions of the two

Na4 cluster [1]. Since then, the method has been

second-order exchange terms. The effects of theapplied to a wide-range of materials in which itmethod with a full GW electron-hole interaction

has demonstrated its accuracy and reliability.

kernel are 0.2 eV maximum for the n à π*

However, the conventional simulations need toexcitations and negligibly small for the πàπ*employ an additional approximation to a GW

GW

= +

electron-hole interaction kernel (Ξ!" ) without

Ξ

exception solving the equation. Specifically, theFigureemployed approximation neglects the twointeraction kernel.

second-order exchange terms in the GW

electron-hole

results of our exciton analysis method suggest

terms have a negligibly small contribution.

that the contribution of the second-order

Therefore, it is evident that the conventional

exchange terms tends to be larger for higher

GW+Bethe-Salpeter method does not satisfy

exciton binding energies.

the Baym-Kadanoff conservation low [2].

References

Considering not only the first-order direct[1] G. Onida, et al., Phys. Rev. Lett. 75, 818

and exchange terms but also the two second-(1995).

order exchange terms, we have developed a[2] G. Baym and P. Kadanoff, Phys. Rev. 124,

GW+Bethe-Salpeter method with a full GW
kernelGW

the those of the conventional method. The

unconfirmed assumption that the second-orderinteraction

Full

+

and π àRydberg excitations compared withelectron-hole interaction kernel based on anelectron-hole1.

+

287 (1961).

and[3] S. Yamada, et al., Phys. Rev. B. 105, 045113

implemented it into our original all-electron(2022).

mixed basis program [3]. Figure 1 shows the[4] M. R. Silva-Junior and W. Thiel, J. Chem.

Feynman diagrams of the full GW electron-hole

Theoryinteraction kernel. We simulated the S1 optical

106

Comput.

6,1546(2010).

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Ab-initio research on nano structures of magnetic nanoparticles
Yasutomi TATETSU
Meio University, Biimata, Nago, Okinawa 905-8585
The search for advanced materials that possess both thermodynamic stability and instability for
different applications has become a hot topic in recent years. With the advancements in technology,
chemical synthesis techniques are being developed to control nanoparticles with precision, allowing
the discovery of new functional materials with unique properties. Teranishi’s team at Kyoto
University has recently made an important breakthrough by discovering a new behavior of crystal
structure transformation in an ionic nanocrystal [1]. Moreover, the team was able to synthesize a new
Z3-type Fe(Pd, In)3 crystal structure by introducing indium as a third element into L12-FePd3, which
could have significant implications for the development of new technologies [2]. The discovery ofthese new materials is critical for advancements in various fields such as medicine, energy,
electronics, etc.
In this report, we have conducted first-principles calculations to explore the stability ofcompounds with Z3-type structures. Our research aimed to estimate the formation energies forvarious Z3-Fe(Pd, M)3 structures, where M represents different transition metals. To perform thesecalculations, we utilized the ab-initio code, OpenMX [3], to compute the ground states of L12-(Fe,M)Pd3 and L12-(Fe, M)Pd3 structures. We compared the energies obtained to analyze the stability ofthese structures. Our results indicated that some Z3-type structures exhibit lower energies comparedto the L12-type structures, indicating that they could be synthesized experimentally. Further
theoretical investigations are required to explain the coercivity mechanism in the Z3-Fe(Pd, M)3
structures.

References[1] Z. Li, M. Saruyama, T. Asaka, Y. Tatetsu, and T. Teranishi, Science, 373, 332 (2021).
[2] K. Matsumoto et al., Nat. Commun., 13, 1047 (2022).
[3] http://www.openmx-square.org

107

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Machine Learning Based Optimized Polymer for Thermal
Function Materials
Bin XU
Department of Mechanical Engineering,
The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8656
Polymer informatic is an emerging filed

simulation.

where the AI and machine learning are beingThe

temperaturedependent

thermalused in the efficient discovery and design ofconductivity of cellulose polymer with thepolymer with target properties and has large

temperature up to 300 K is shown in Fig. 2. The

impact on several aspect of human life, science,

thermal conductivity of filaments with lower

and technology. Cellulose is an organic

crystallinity index (CI) also increases with acompound with the formula (C6H10O5)n, a

similar slope until 240 K, but then saturates to apolysaccharide consisting of a linear chain of

constant value until 300 K. Temperature-

several hundred to many thousands of β linked

dependence thermal conductivity reveal that the

D-glucose units. Cellulose is an importantfilaments exhibit phonon transport at effective

structural component of the primary cell wall ofdimension between 2D and 3D.

green plants, many forms of algae and theoomycetes. In this work, we applied themolecular dynamics simulation in combination
with the machine learning to explore the effectof strain, hydrogen bonds, and structure orderingon thermal transport in cellulose. The structure
for the simulation is shown in Fig. 1.

Figure 2 temperature dependent thermal
conductivity of cellulose nanofiber.

References[1] Mu, Fengwen, Bin Xu, Xinhua Wang, Runhua Gao,Sen Huang, Ke Wei, Kai Takeuchi et al. Journal of
Alloys and Compounds 905 164076 (2022).
[2] Wang, G., Kudo, M., Daicho, K., Harish, S., Xu, B.,
Shao, C., Lee, Y., Liao, Y., Matsushima, N., Kodama, T.
and Lundell, F., Nano Letters, 22, 8406-8412 (2021).

Figure 1 Schematic illustration of structure usedfornon-equilibrium

molecular

dynamics

108

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Theoretical study of quantum eﬀects innanographene devices
Koichi KUSAKABE
Graduate School of Science, University of Hyogo
Kouto, Kamigori, Hyogo 5-8581

1

Introduction

New quantum devices for quantum information and quantum electronics are being developed by exploring various quantum effects. We are studying graphene-based quantum computation resources,[1] and graphenebased spin-electronic devices.[2, 3] We havedeveloped theoretical approaches to study
stubility of adsorbed molecules on graphene
in solvents,[4] acceleration methods utilizing
evolutionary algorithms,[5] and light-induced
structural transformation of nanomaterials.[6]2Quantum computation resources by poly-PTM

The design of a hydrogenated poly-PTM
measurement-based quantum computation resources was promoted by evaluating decice behavior based on proton NMR simulations on
adsorbed hydrogen on the poly-PTM. The design of a two-dimensional S = 3/2 antiferromagnetic Heisenberg system was promoted
based on the design of a two-dimensional
honeycomb poly-PTM network.
We concluded that possible computational resource
states in a two-dimensional hydrogenatednanographene requires control of the eﬀective many-body interaction between localized
spins.

3

Stability and functions of
nanographene devices

The structural stability of hydrogenatednanograpehe structures was evaluated, as wellas the activation energy barrier heights on thehydrogen diﬀusion path. To evaluate decoherence, which is the central concern of quantum information operation, we developed atheory of spin-current-rotation coupling andthe eﬀect of inserting insulating hBN between
metallic Ni substrate and graphene. We concluded that control of the decoherence process
requires the control of the interaction between
electron spins and current rotation.

Acknowledgement
The author acknowledges the big contributionsof Dr. N. Morishita, Mr. K. Komatsu, andProf. M. Kitatani on nanographene quantum computation resorces. He also thanks
Dr. Y. Wicaksono, Ms. H. Harfah, Dr. G.
K. Sunnardianto, and Prof. M. A. Majidi forcollaboration on graphene quantum spintronics devices, and all collaborators for theoretical
approaches to nanomaterial device structures.

References[1] N. Morishita, Y. Oishi, T. Yamaguchi,
K. Kusakabe, Appl. Phys. Express 14,
121005 (2021).
[2] Y. Wicaksono, H. Harfah, G. K. Sunnardianto, M. A. Majidi, and K. Kusakabe,
RSC Adv., 12, 13985 (2022).
[3] Y. Wicaksono, H. Harfah, G. K. Sunnardianto, M. A. Majidi, and K. Kusakabe,
Magnetochem. 9, 113 (2023).
[4] Y. Oishi, H. Ogi, S. Hagiwara, M. Otani,
and K. Kusakabe, ACS Omega, 7, 35,31120 (2022).
[5] T. Ishikawa, K. Kusakabe, Y. Makino, S.
Sakamoto, N. Okuyama, J. Phys. Chem.
A, 126, 8082 (2022).
[6] M. Nagai, Y. Higashitani, M. Ashida,
K. Kusakabe, H. Niioka, A. Hattori, H.
Tanaka, G. Isoyama, N. Ozaki, Commun.
Phys., in press (2023).

109

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo!"#$%&'$(#)*$+",&#+$%*'-,+-+"./*/,0/*-1,2#-/*%.,30-$%*'-+",$+"$0"+%*'-,4,*)5"*$*%,/'"6+%*'-,)'2#" 7!"#$%&'()&*&!"#$%$&$'()*+(,*-%.(,$/$'(012#%3#4
51'(6"%7'+#%$2(*)(5*82*4(9/#1%:/;"*;1/4(9/#1%:/4(<1%=/(>??;@A@B%*'./0(+.1#.1#2"(%3#')/<%?"'9!"#$%&"#'()*+"*#,!-#%*'./0(+.1#2.1.3%4"/#.1# 5(677789 :$"# )1*"/'(%1*+1;# .<# ($+'# 2"(%3=>%("/ +1("/<%?"# '4'("2# +'# +20./(%1(# <./# @%'+?#"3"?(/.?$"2+?%3# /"%?(+.1'# ')?$# %'# ($"# .A4;"1#/"*)?(+.1#/"%?(+.19#:$+'#4"%/B#>"#<.?)'"*#.1#($"#?%3?)3%(+.1# 2"($.*# <./# ($"# '"?.1*C./*"/#
1.13+1"%/#')'?"0(+@+3+(4#!"!# +1#($+'#'4'("29#D+;9# 7%# '$.>'# ($"# 2.'(# '(%@3" %*'./0(+.1#
2.1.3%4"/#'(/)?()/"B#EFGHEFG ?"33 +1?3)*+1;#IC%1*# JC2"2@"/"*# /+1;'.1# 5(677789

K4#"203.4+1;# ($"# *"1'+(4# <)1?(+.1%3# 0"/()/@%(+.1#($"./4 ?.2@+1"*# >+($# ($"# "<<"?(+&"# '?/""1+1;#
2"*+)2 L7M N# "A03+?+( "3"?(/+?# <+"3*# %00/.%?$B#>"#.@(%+1"*#!!"#%'#'$.>1#+1#D+;9#76@89 LOM :$"#!!!
1";%(+&"# %1*# 0.'+(+&"# '+;1'# .<# P2# !!"#
+1# ($"#!!!
$+;$#6FQQQRFIQQ#?2$%8#%1*#3.>#6SQQ ROTQQ#?2$

D+;9#7W#6%8#,!-#%*'./0(+.1#2.1.3%4"/#'(/)?()/"9#%9#
6@8#X"?.1*C./*"/#1.13+1"%/#')'?"0(+@+3+(4#!!"#!!!

8# </"U)"1?4# /";+.1'B# /"'0"?(+&"34B# >"/"#.@(%+1"*9# :$"# '0"?(/%# '$.># 1";%(+&"# *.)@3"#
0"%V'# +1# ($"# $+;$C</"U)"1?4# /";+.1B# ?.1'+'("1(#!"#"$"%&"'>+($# ($.'"# .@(%+1"*# @4# $"("/.*41"C*"("?("*#

L7M# Y9# -(%1+# %1*# -9# X);+1.W# 5$4'9# Z"&9# K# !"')2C</"U)"1?4# ;"1"/%(+.1# '0"?(/.'?.049 LFM

6OQQS8#77ITQJ9#:$"# 0"%V' %(# %00/.A+2%("34# OQQQRFIQQB# 7SQQB#

LOM [9# ,%/)4%2%B# :9# X);+2.(.B# %1*# -9# X);+1.B#%1*# SQQR7OQQ ?2$% %/" ?.20.'"*# .<# ($"# -,#"#$%&''()*\ $((0'W==+''01'C;+(3%@9+''09)C(.V4.9%?9'(/"(?$+1;B# ,!-# @"1*+1;B# %1*# ,!-# 3+@/%(+.1#]0=]C$%/)4%2%=5(777^,O-C2.1.3%4"/

2.*"'B# /"'0"?(+&"349 :$+'# '()*4# 0/.&+*"'# %#LFM#:9#X);+2.(.#%1*#_9#Y%(')2.(.B#5$4'9#`$"29#<)1*%2"1(%3#@%'+'#<./#'(/)?()/"#%1%34'+' .<#,!-#`$"29#5$4'9#OOB#7STIF#6OQOQ89#

110

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Discovery of Fast Anhydrous Proton Conduction in α-MoO3
Atsuo YAMADA
Department of Chemical System Engineering,
The University of Tokyo, Bunkyo-ku, Hongo, Tokyo 113-8656
Aqueous proton batteries show potential as

calculate proton diffusion pathways.

high-power energy storage devices because of

The energy profiles of H+ diffusion in

the small size of the protons and their high

MoO3 are shown in Fig. 1. At the early stages ofmobility in aqueous solutions. Among various

protonation and deprotonation, the activationtransition metal oxides, orthorhombic MoO3 (α-barriers for proton diffusion between the oxygenMoO3) is one of the most promising proton hosts

arrays of MoO3 were below 0.4 eV, which iswith fast kinetics and large capacities. Herein,comparable to that of Grotthuss mechanism in

we investigated the mechanism of proton

water. Therefore, further exploration of otherintercalation and transportation in α-MoO3

host materials with dense and continuous

based on theoretical calculations [1].

oxygen arrays capable of fast anhydrous proton

The spin-polarized DFT calculation was

conduction is an important challenge for the

adopted to trace the H+ dynamics in α-MoO3practical application of high-power aqueous

with VASP program. Based on a 2 × 1 × 2

proton batteries.

supercell (Mo16O48), a series of H-intercalated

References

structuresof

HxMoO3 (0 ≤ x ≤ 3.0)

was[1] Z. Ma, X. M. Shi, S. Nishimura, S. Ko, M. Okubo, A.

calculated. The CI-NEB method was used to

Yamada, Adv. Mater., 34, 2203335 (2022).

Figure 1 Anhydrous proton transport via hydrogen bond network and corresponding energy barriers at the earlystages of protonation (a) and deprotonation (b). The three most stable H+ absorption sites in MoO3 are labeled as
A, B, and C, respectively.

111

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First-principles prediction of anomalous Hall effectin antiferromagnetic perovskites
Kunihiko Yamauchi
Department of Precision Engineering, Graduate School of Engineering,Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871
Recently, large anomalous Hall conductivity
innoncollinear

antiferromagnetic(AFM)

systems has been recently observed and attracted
much attention while the anomalous Hall effecttakes place typically in ferromagnetic (FM)materials with the finite magnetization.
In this study, by using DFT calculation, we
predict the anomalous Hall effect (AHE) in
perovskite CaCrO3 as a representative ofcollinear antiferromagnetic materials [1]. We
showed that the C-type AFM ordering (Fig. (a))
generatesthesizableanomalousHallconductivity and revealed two essential roles of
the non-symmorphic Pbnm symmetry. (i) The
screw and glide symmetry operations bind the

Figure: (a) C-type AFM ordering in Pbnm

AFM and FM order parameters in the same

crystal structure of CaCrO3. (b) Berry curvatures

irreducible representation so that AHE is activein the orthorhombic Brillouin zone.

in the AFM order. (ii) The band-sticking effectat the Brillouin-zone surface makes Cr-t2g state

References

form the narrow bands near the Fermi energy; a[1] Thi Phuong Thao Nguyen and Kunihikocouple of those bands cause anticrossing andYamauchi, Phys. Rev. B. 107, 155126 (2023).

enhancement of the Berry curvature (Fig. (b)).

[2] A. D. Vita, T. T. P. Nguyen, .., K. Yamauchi,

In this year, we also studied 2D magnetism

et al., Nano Letter 22, 7034 (2022).

as reported in Ref. [2] and [3] in collaboration[3] K. Riedl, .., T. T. P. Nguyen, K. Yamauchi, etwith theoretical and experimental groups in Italyal., Phys. Rev. B 106, 035156 (2022).

and Germany.

112

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Ab-initio electron dynamics simulation for nonlinear opticalphenomena in solids
Shunsuke SATO
Center for Computational Sciences
University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan
We have studied light-induced electron

both delocalized and bound electrons in solids.

dynamics in solids based on the time-dependent

We further plan to apply the developed method

density functional theory. This year, we focusedfor the investigation of nonlinear opticalon the development of a novel analysis

phenomena in solids in order to develop thetechnique for light-induced linear and nonlinear

microscopic interpretation of perturbative andoptical phenomena in solids based on the time-nonperturbative nonlinear phenomena.

dependent current density [1]. Here, we first

In addition to the development of the above

compute the time-dependent current density as

theoretical scheme, we have studied transient𝒋(𝒓, 𝑡) = −optical properties of solids under the presence𝑒∫ 𝑑𝒌 ∑ 𝑓𝑏𝒌
Ωme 𝐹𝐵𝑍𝑏∗(𝒓,× 𝜓𝑏𝒌(𝒓,𝑡)(𝒑 + 𝑨(𝑡)) 𝜓𝑏𝒌 𝑡),

of intense femtosecond laser fields. For the(1)

investigation of nickel, we analyzed the light-

where 𝜓𝑏𝒌(𝒓, 𝑡) are time-dependent Kohn-

induced spin-dynamics and transient absorption

Sham orbitals, and 𝑨(𝑡) is the vector potential

based on the pump-probe technique, and wethat is related to the applied external electric

found that the local spin-density dynamics𝑡field as 𝑨(𝑡) = − ∫ 𝑑𝑡 ′ 𝑬(𝑡 ′ ) . By applying

causes the change of the photoabsorption

Fourier transform to the computed current

around the absorption edge. Based on thisdensity in Eq. (1), we obtain the frequency-

finding, we further proposed the attosecond

resolved microscopic current density as

spin-dynamics spectroscopy to investigate the𝒋̃(𝒓, 𝜔) = ∫ 𝑑𝑡 𝑒 𝑖𝜔𝑡 𝒋(𝒓, 𝑡).

element-specific light-induced spin-dynamics(2)

The frequency-resolved microscopic current

in the attosecond time-scale.

density contains the atomic scale real-space

References

information of light-induced linear and optical[1] Shunsuke A. Sato, “"Frequency-resolved

phenomena. In Ref. [1], we demonstrated thatmicroscopic current density analysis of linear

the developed current-density analysis capturesand nonlinear optical phenomena in solids",

microscopic insight into optical phenomena inarXiv:2302.05859

113[cond-mat.mtrl-sci].

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First-principles study on the structure of water/grapheneinterfaces depending on the number of graphene layers
Tatsuhiko OHTO
Graduate School of Engineering Science,
Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531
Ab initio molecular dynamics (AIMD)

The simulations were performed using thesimulation is a powerful tool to describe

CP2K code [3]. After obtaining the sufficientheterogeneous systems such as the water/solid

length of trajectories, we are going tointerface. Although AIMD is computationallyconstruct machine learning force field toexpensive, it describes electronic states beyond

simulate larger scale interfaces between

classical force fields,[1] which is important for

carbon/water.

interfacial structures.
We have simulated sum-frequency generation(SFG) spectra of isotopically diluted water at thewater-graphene sheet interfaces using ab initio
molecular dynamics simulations, with changing
the number of the graphene layers from one tothree. The simulation of water-single grapheneinterface has already been performed using

Fig. 1: Structures of the graphene-water

ISSP.[2] A sharp ‘dangling’ O-D peak aroundinterface.

~2640 cm-1 appearing in simulated SFG spectra
evidencesthatgrapheneisReferenceshydrophobic.

Although the results obtained so far are still[1] T. Ohto et al. Phys. Chem. Chem. Phys. 19,

preliminary, the dangling O-D peak is blue-

6909 (2017).

shifted by increasing the number of the graphene[2] T. Ohto et al. Phys. Chem. Chem. Phys. 20,

layer. This frequency difference manifests a

12979 (2018)

weaker O-D…C intermolecular interaction[3]

between water and thicker graphene, indicating

http://cp2k.berlios.de/that graphite is more hydrophobic than graphene.

114

The

CP2K

developersgroup,

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Multiscale simulations for magnetic materials
Hung Ba TRAN
Laboratory for Materials and Structures, Institute of Innovative Research,
Tokyo Institute of Technology, Midori-ku, Yokohama 226-8503, Japan

In our recent works, we consider the spin reorientation effect in Nd2 Fe14 B material basedon first–principles calculations[7]. Nd2 Fe14 B isfamous material for permanent magnets thatare widely used in power generation, motors,
and so on due to the high magnetic anisotropy
energy, saturation magnetization, and Curie
temperature. However, the material has aproblem at low temperatures. It is spin reorientation from the easy axis (z–direction) tocone magnetization (where the magnetization
is tilted). In previous theoretical works, thespin reorientation in Nd2 Fe14 B is usually considered using empirical parameters as crystal–electric field term. In contrast, in this work,
we calculate the Dzyaloshinskii–Moriya interactions (DMI) by first–principles calculations
for the first time. We found that the DMI is
the origin of spin reorientation in Nd2 Fe14 B.
The spin reorientation in Nd2 Fe14 B can be re-

(b)

25

Mtotal
Mz
Mxy

20
15

𝑧

10

𝜃

15

𝑀

10
5

5

0

0

0.8
0.6
0.4
0.2

50 100 150
T (Κ)

0

0
0

200

(c)

400

T (Κ)

4

600

800

1000

0.2

0.4

0.6
T/TC

4

1
0

0.8

1

1.2

H = 1TH = 2TH = 3TH = 4TH = 5T

3∆SR (J/kg.K)

2

0

(d)
H = 1TH = 2TH = 3TH = 4TH = 5T

3−1

Monte Carlo
DFT
EXP(raw)
EXP(fit)1

w DMI
w/o DMI20θ (degree)

M (µB/f.u)25

MAE(T)/MAE(max)(a)∆SR (J/kg.K)

Our previous works have studied the magnetocaloric effect by multiscale simulations. The
magnetic parameters, such as magnetic exchange coupling constants Jij and magnetic
anisotropy energy, are calculated from first–
principles calculations. Then, the Monte-Carlosimulations are performed to estimate themagnetic properties at thermal equilibrium,
such as magnetization curves and isothermal
magnetic entropy change. Several types ofthe magnetocaloric effect, such as conventional(direct) magnetocaloric effect, giant magnetocaloric effect, anisotropic magnetocaloric effect, and inverse magnetocaloric effect, can beconsidered using our in–house program[1, 2, 3,
4, 5, 6].

2
1
0

0

50

100
T (K)

150

200

−1
650

700

750
T (K)

800

850

Figure 1: (a) Total (red) and projected spontaneous magnetization in the out–of–plane(green) and in–plane (blue) directions as afunction of the temperature of Nd2 Fe14 B (inset figure: Temperature dependence of thetilt angle between the magnetization direction and z–direction in the case of switching on (red) and switching off (black) the
Dzyaloshinskii–Moriya interaction). (b) The
magnetic anisotropy energy obtained is basedon DFT (green) and Monte Carlo method byintegrating the M –H curves (black) and experimental raw data (red dots) and that derived from anisotropy constants (continuous
red curves). (c) Rotating magnetic entropychange as a function of temperature and themagnetic field around the spin reorientation
temperature and (d) Curie temperature.

produced in Figure. 1 (a) by introducing the
DMI. In addition, the peak like in magnetic
anisotropy energy of experimental work can be115

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

dition, two types of helicity can be realized
in CrCl3 and VCl3 due to the nearly opposite direction in Dzyaloshinskii–Moriya vector.
Not only skyrmions but also antiferromagnetic
skyrmions and meron are observed in MX3 .

References

Figure 2:
In–plane components of
Dzyaloshinskii–Moriya vector for the second nearest neighbor of intra–layer of CrCl3
and VCl3 , where the red and green arrows
indicate pairs with different sub–lattices, andthe yellow arrow indicates the direction of
Dzyaloshinskii–Moriya vector. Snapshot ofMonte Carlo simulation of CrCl3 at 10 K withexternal magnetic field equal to 4 T along
the positive z–direction. Snapshot of theMonte Carlo simulation of VCl3 at 10 K withexternal magnetic field equal to 6 T along thepositive z–direction.

reproduced quantitatively, as shown in Figure.
1 (b). The spin reorientation in the material
is usually not desirable for the application ofa permanent magnet. However, we found thatthe rotating entropy change around spin reorientation is almost four times higher than at
the Curie temperature, as shown in Figure. 1(c, d) due to the peculiar properties of spin
reorientation.
On the other hand, we also consider theskyrmions in van der Waals centrosymmetric MX3 (M: V, Cr, Mn, Fe, Co; X: Cl, Br,I) [8]. Skyrmions usually appear in non–centrosymmetric material due to the DMI lacking inversion symmetry. Although MX3 has
space group R3̄ being centrosymmetric withhoneycomb lattice, the broken local inversion
symmetric of the second nearest neighbor of
intra–layer stabilizes the finite DMI. It leads
to ultra-small skyrmions with a diameter being 2.0 nm, as shown in Figure. 2. In ad-[1] H. B. Tran, T. Fukushima, K. Sato, Y.
Makino, and T. Oguchi, J. Alloys Compd.
854, 157063 (2021).
[2] H. B. Tran, T. Fukushima, Y. Makino,
and T. Oguchi, Solid State Commun. 323,
114077 (2021).
[3] H. B. Tran, T. Fukushima, H. Momida, K.
Sato, Y. Makino, and T. Oguchi, Comput.
Mater. Sci. 188, 110227 (2021).
[4] H. B. Tran, T. Fukushima, H. Momida,
K. Sato, Y. Makino, and T. Oguchi, J.
Alloys Compd. 926, 166718 (2022).
[5] H. B. Tran, H. Momida, Y. Matsushita,
K. Sato, Y. Makino, K. Shirai, and T.
Oguchi, Phys. Rev. B 105, 134402 (2022).
[6] H. B. Tran, H. Momida, Y. Matsushita,
K. Shirai, and T. Oguchi, Acta Mater.
231, 117851 (2022).
[7] H. B. Tran, Y. Matsushita, Appl. Mater.
Today 32, 101825 (2023).
[8] H. B. Tran, Y. Matsushita, Skyrmions in
van der Waals centrosymmetric materials
with Dzyaloshinskii-Moriya interactions,
arXiv:2209.02333 (2022) (under review).

116

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Theoretical design of novel functional materials
based on abundant elements for energy conversion
and storage.
Tetsuya TAKETSUGU
Faculty of Science, Hokkaido University
N10W8, Sapporo, Hokkaido 060-0810

DFT computations using QuantumEspresso
and VASP packages are used to gain physicalchemical insights and to design novel functional materials with abundant elements forenergy conversion and storage processes. This
helps to reduce costs and dependences on precious metals in energy-related industrial applications.
We have demonstrated that oxygenfunctionalized hexagonal boron nitride (h-BN)
can be an efficient catalyst for oxidative
dehydrogenation (ODH) of light alkanes,
specifically ethane (C2 H6 ), propane (C3 H8 ),butane (C4 H10 ), and isobutane (HC(CH3 )3 )[1]. This is particularly interesting because
it is commonly accepted that hexagonalboron nitride is an inert material. However,
we have demonstrated that h-BN can befunctionalized and serve as an active catalyst.
It has been found that the reaction pathway
involves two H atom transfer steps with small
activation energies. We demonstrate thatthe synergy of two active sites, B-O-O-B and
B-O-B (Figure 1), is crucial for the first andsecond H-transfer, respectively. With theincrease in molecular mass of the consideredlight alkanes, the ODH reaction temperature
decreases. The charge redistribution during
H-transfers and localized oxygen atomic
states in the conduction band are explored tosuggest possible descriptors for the rational
design of new catalysts. The universal actionof the B-O-O-B and B-O-B active sites for

ODH of the light alkanes paves the way formetal-free BN-based materials for future catalytic applications.

Figure 1: Catalyticaly active (a) B-O-O-B and(b) B-O-B sites [1].
The multielectron transfer is a key process being indispensable for a wide spectrumof modern electrochemical energy technology.
Finding promising multielectron-transfer materials with abundant elements is especially
important to advance the present rechargeable battery technology mainly relies on single electron transfer type electrodes. To date,117

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

a few oxide-based materials can reach an
electron-transfer number per metal-cation (e)larger than 2 upon a (de)intercalation mechanism. However, these oxide-based electrodes
face challenges in improving electrochemical
properties as they still suffer from considerable degradation during charge/discharge reactions due to irreversible rearrangements ofbonds between metallic cation and anionic
oxygen. Furthermore, these high-performance
multielectron-transfer oxides are mainly constituted of precious metals. As it is wellknown, these elements are limited, and therefore, electrode materials based on precious
metals are inappropriate candidates to replace
the present cathode materials.

work provides new insights into the affordable
multielectron-transfer electrodes and uncovers
an alternative strategy to advance the electrochemical energy storage reactions.

References[1] S. Kumar, A. Lyalin, Z. Huang, and T.
Taketsugu, Catalytic Oxidative Dehydrogenation ofLight Alkanes over Oxygen Functionalized Hexagonal Boron Nitride, ChemistrySelect 7, e202103795(2022).
[2] D. Xia, K. Sakaushi, A. Lyalin, K. Wada, S. Kumar, M. Amores, H. Maeda, S. Sasaki, T. Taketsugu and H. Nishihara, Superior MultielectronTransferring Energy Storage by π-d Conjugated
Frameworks, Small 18, 2202861 (2022).

Figure 2:
A significantly reversible
multielectron-transfer is demonstrated byusing only abundant elements [2].
We have demonstrated that the non-oxide
electrode based on the bis(diimino)copper
framework can show e = 3.5 with cation/anion
co-redox mechanism together with a dual-ion
mechanism (Figure 2). In this study, the roleof the cation-anion interactions has been unveiled by using an experiment/theory collaboration applied to a series of the model nonoxide abundant electrode systems based on different metal-nitrogen bonds. These models
provide a route for the design of the multielectron transfer materials based on the tunable πd conjugated electronic structures. It is foundthat the Cu-nitrogen bonds show a unique reversible rearrangement upon Li-intercalation,and this process responds to acquire a significant reversible multielectron-transfer. This118

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Microscopic structure of water/CeO2 interface
Akira NAKAYAMA
Department of Chemical System Engineering,
The University of Tokyo, Tokyo 113-8656, JAPANThe structural properties and dynamical

calculations. For each interface, a slab model

behaviors of the interface between water andwas created with a surface size of p(4×4) and acerium oxide (CeO2) are investigated bywater thickness of 30 Å. The conventional andreactive molecular dynamics (MD) simulations

enhanced sampling DFT-MD methods were

employing

performed for 20 ps, yielding a total of 8000

theneuralnetwork

potentials(NNP)[1][2] that are trained to reproduce

configurations as training data.

density functional theory (DFT) results. In the

We mainly discuss the results for thewater/CeO2(110)DFT-based MD (DFT-MD) simulations withprojection of the density distribution of O andenhanced sampling schemes[3] are employed to

H atoms on the CeO2 surface and in the firstefficiently acquire training data that include

hydration layer obtained from the NNP-MD

diverseconfigurations

simulations is shown in Figure 1. Note that thecaused by proton hopping events. The water

average position of the topmost Ce atoms is set

interfaces with three low-index surfaces of

to z = 0 Å.

(111), (110), and (100) are explored with thesesimulations provide sufficient statistics toneural network potentials, and structure andanalyze the interface structure in more detaillong-range proton and hydroxide ion transfer

compared to previous studies.

hydrogen-bonding

dynamics are examined with unprecedented

interface.

Thez-axisconstruction of the neural network potentials,

The large-scale and long-time

The mechanism of proton transfer occurring

system sizes and long simulation times.

at the water/CeO2(110) interface is then

The time step for DFT-MD simulations wasanalyzed. Proton transfer can be roughly

set to 1 fs and the temperature was controlled at

divided into surface proton transfer (SPT), in

360 K. RPBE was used for the DFT functional.

which proton transfer occurs between a water

The DFT-D3[4] and DFT+U methods was usedmolecule coordinated on a Ce atom and a

to

and

surface OS atom, and adlayer proton transfer

electron correlations for Ce 4f electrons,(APT), in which proton transfer occurs between

respectively. k-point sampling was performedaat Γ points only. CP2K was used for DFT-MD

coordinated on a Ce atom. The proton transfer

incorporate

dispersion

interaction

119

water

moleculeandhydroxideion

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

between surface species is further classifiedhydrogen bonds with neighboring surface O

into direct and indirect transfer that involves

atoms, thus making SPT less likely to occur. In

neighboring water molecules.

addition, more migration was observed in the y-

At the water/CeO2(110) interface, a total of

direction since the distance between surface Ce

863 proton transfers were observed during 4 ns

is closer.

of trajectory, when thermal equilibrium isconsidered to have been reached.

In summary, we have generated training

Table 1

data using DFT-MD calculations with enhanced

shows the observed ratio of each proton transfer.

sampling scheme and constructed NNPs for the

Since the structure of the CeO2(110) surface iswater/CeO2 interface.

not symmetric with respect to the x-axis and y-

NNPs, we performed large-scale and long-timeaxis directions, the APT were also classified

simulations

according to whether the proton transfer

information on the structure and proton transfer

occurred in the x- or y-axis direction.

mechanisms near the interface, which wereandUsing the constructedobtainedthedetaileddifficult to obtain using the conventional DFTMD simulations.
Table 1: Proton hopping machanims

References[1] J. Behler and M. Parrinello, Phys. Rev. Lett.
98, 146401 (2007).

Fig. 1: Density profiles of oxygen andhydrogen at the water/CeO2(110) interface.

[2] J. Behler, J. Chem. Phys. 145, 170901(2016).

As seen, APT occurred more frequently than[3] L. Maragliano and E. Vanden-Eijnden,

SPT, possibly because the distance between

Chem. Phys. Lett. 426, 168 (2006).

surface O atoms is closer. H atoms bonded to[4] S. Grimme, J. Antony, S. Ehrlich, and H.

surface O atoms are stabilized by forming

Krieg, J. Chem. Phys. 132, 154104 (2010).

120

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Prediction of properties of organic ferroelectrics andpiezoelectrics by ﬁrst-principles calculation
Shoji ISHIBASHI
National Institute of Advanced Industrial Science and Technology (AIST)
Tsukuba, Ibaraki 305-8568

Many azole compounds such as imidazoles
and tetrazoles contain polar and bistablehydrogen-bonded molecular sequences suitable for ferroelectricity or antiferroelectricity.
In this study, we have computationallyinvestigated relative stabilities of possible ferroelectric (FE) and antiferroelectric(AFE) phases in eight azole compounds:
5-(4-aminophenyl)-1H-tetrazole(APHTZ),
5-(4-ﬂuorophenyl)-1H-tetrazole(FPHTZ),
5-(4-chlorophenyl)-1H-tetrazole(CPHTZ),
5-(4-methylphenyl)-1H-tetrazole (MPHTZ),
5-phenyl-1H-tetrazole (PHTZ), 2-phenyl1H-imidazole (PHIM), 2,4,5-tribromo-1Himidazole (TBIM), and 2-bromo-4,5-dimethyl1H-imidazole (DM-BIM) [1].
The Perdew-Burke-Ernzerhof (PBE) version of the generalized gradient approximation (GGA) [2] was used to describe the electronic exchange-correlation energy. Startingfrom the experimental structures, the atomic
positions were optimized. The convergence criterion was set to 5 × 10−5 Ha/bohr for the
maximum force. All the calculations were performed using the QMAS code based on the
projector augmented-wave method [3] and theplane-wave basis set.
The obtained results are shown in Fig. 1 together with schematic illustrations of various
states in a zero ﬁeld based on the polarization
hysteresis experiments as well as the structural
analyses. It is conﬁrmed that the computational and experimetal results are consistent
with each other.

Figure 1: Ferroelectric (FE), antiferroelectric (AFE), and phase-coexisting (F/A hybrid and A/A hybrid) states in the PHTZs
crystals. (a) Schematic illustrations of various states in a zero ﬁeld. (b) Energy histogram of the candidate H-ordered states relative to each FE (or FE-I) form. Filled andopen circles point to the non-degenerate andnearly degenerate ground states, respectively(S. Horiuchi, H. Minemawari, and S. Ishibashi,
Mater. Horizons, Advance Article (2023),
DOI: 10.1039/d2mh01530g).

References[1] S. Horiuchi, H. Minemawari, and S.
Ishibashi, Mater. Horizons, Advance Article (2023).
[2] J.P. Perdew, K. Burke, and M. Ernzerhof,
Phys. Rev. Lett. 77, 3865 (1996).
[3] P. E. Blöchl, Phys. Rev. B 50, 17953(1994).

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Ab initio calculation for transition-metal-oxideinterface
Kazuma NAKAMURA
Quantum Physics Section, Kyushu Institute of Technology
1-1 Sensui-cho, Tobata, Kitakyushu, Fukuoka, 804-8550

In the present study, we investigate electronic structures of interfaces and surfaces oftransition-metal oxides using first-principles
calculations based on density functional theory. In material science, important physicalproperties often occur at the surfaces and interfaces of materials. Currently, it is possible
to predict physical and chemical properties ofthe surfaces and interfaces by using ab initio
electronic structure calculations. On the other
hand, since the size of systems including thesurface and interface becomes large, and complicated problems such as the generation of an
artificial electric field in the surface system are
known to occur, so it is necessary to accumulate detailed know-how to perform stable abinitio calculations.
Here, we focus on a representative oxide interface system of SrTiO3 /SrVO3 (STO/SVO)
hetero structure, and study its microscopic
electronic structure and optical properties indetails. The STO/SVO system is a fascinating
system realizing a metal-insulator transition at
the interface [1, 2], especially small thicknessof SVO block. In this study, we analyzed thissystem with ab initio calculations, particularly
focusing on optical spectra.
We performed ab initio self-consistent pseudopotential calculations in which wavefunctions were expanded in a plane-wave basis [3].
We employed norm-conserving pseudopotentials, and the generalized-gradient approximation for exchange-correlation energy. The cutoff energies for the wavefunction and charge

density are 100 Ry and 400 Ry, respectively.
The atomic geometry was fully optimized withkept at inplane lattice constant a = 3.9451
Å, where the force on each ion were relaxed
to less than 0.001 a.u. and a MonkhorstPack k-grid of 10×10×2 was employed. Optical calculations were performed with using
RESPACK [4]: The energy cutoff for the polarization function was set to 3 Ry. The total
number of bands considered in the polarization
calculation was determined to cover unoccupied states up to 10-15 eV above the Fermi
level. The integral over the Brillouin zone
was calculated with the generalized tetrahedron technique with a smearing of 0.01 eV.
Figure 1 shows our structure models tobe analyzed, where we consider a multilayer
model consisting of SVO and STO blocks[panel (a)] and a symmetric slab model [(b)].
The slab model includes a vacuum region with
20 Å, and has a mirror symmetry with respect
to the central TiO2 plane. Then, artificial electric fields due to asymmetry in the both ends ofthe slab do not occur. The structural models
include several SVO blocks, where we specify
the number of the blocks as n. In the present
study, we consider the structural models until
n = 4. The number of the STO blocks was
fixed to 5 for the multilayer and 8.5 for theslab (0.5 indicates an additional central TiO2
layer).
Figure 2 shows our calculated band structures of the multilayer (a) and slab (b), where
the band diagrams for the n = 2 model are

122

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo(a) Multilayer

SrTiO

SrVO

unitcell(b) Symmetric slab
vacuumvacuumFigure 1: Two structural model: (a) Multilayer consisting of SrTiO3 and SrVO3 blocks. (b)Symmetric slab having a mirror symmetry with respect to the center TiO2 plane.

[1] D. H. Kim, et al., Solid State Commun.
114, 473 (2000).
[2] K. Yoshimatsu, et al., Phys. Rev. Lett.
104, 147601 (2010).
[3] P. Giannozzi, et al., J. Phys. Condens.
Matter 29, 465901 (2017).
[4] K. Nakamura, et al., Comput.
Commun. 261, 107781 (2021).

Phys.

Energy (eV)2(a) Multilayer

1
0

-1
-2

Energy (eV)32(b) Symmetric slab

1
0

-1
-2
-3
R
G
1
(c) Multilayer

X

Reflectivity

Figures 2(c) and (d) show our calculated reflectance spectra for the multilayer and slab
models, respectively. We see that the both
spectra exhibit a lowering of the plasma edge
compared to the bulk SVO one. We also see
the appreciable n dependence of the plasmaedge. The difference between the multilayerand slab results seems to be small.
In summary, we have calculated electronic
structures and reflectance spectra of the
STO/SVO system from first principles. In theexperiments, the system is insulator at small
thickness of the SVO blocks, and this origin
should be further investigated from view ofstrongly correlation physics. To this end, weneed to perform ab initio derivations of thelow-energy model of SVO-t2g bands near theinterface, which leaves as a next step.

3

0
1

M

GZ

1

10

n=1
n=2
n=3
n=4
Bulk SVO

0.5

Reflectivity

displayed. From the band structures, we seethat both two systems are metal; the SVO t2gbands cross the Fermi level, and the STO t2gband is about 1 eV above from the Fermi level.

(d) Symmetric slab

0.5

0
0.01

0.1

Enegy (eV)Figure 2: Our calculated band structures of(a) multilayer and (b) slab model where weshow n = 2 case. Panels (c) and (d) show ourcalculated reflectance spectra of multilayer andslab models, respectively.

123

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Two-color mixing enhancement of nonlinear opticalabsorption for a semiconductor
Yasushi SHINOHARA1,2, and Mizuki TANI3
1

Photon Science Center,

The University of Tokyo, Hongo, Bunkyo-ku, Tokyo 113-8656.
2

NTT Basic Research Laboratories,

NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198.
3

Department of Nuclear Engineering and Management

The University of Tokyo, Hongo, Bunkyo-ku, Tokyo 113-8656.

We have theoretically studied dielectrics

energy absorption of Si crystals exposed to

exposed to strong laser fields using a first-

strong laser field that has two color components,

principles theoretical framework based on time-

1.6 eV, and 3.2 eV [2]:dependent density-functional theory (TDDFT)

𝐸(𝑡) = 𝑓(𝑡) [𝐸1.6 eV cos (within SALMON-TDDFT [1]. The real-time
solution of the time-dependent quantum system

1.6 eV𝑡)ℏ+ 𝐸3.2 eV cos (is one of the most accurate schemes to evaluate

3.2 eV𝑡)],ℏnonlinear optical responses that perturbative

where 𝑓(𝑡) is an envelope function that has 14.4

expansions cannot describe.

fs full-width-half-maximum. The second color,

One of the most prominent features of the

3.2 eV, is slightly above the direct gap of thenonpertubative response of the material is the

crystal. To model an experimental setup ofcomplex mixing of multiple frequencies of an

frequency conversion for 3.2 eV from 1.6 eV via

applied field. We do not have a simple formulasecond harmonic generation, we change theto model the response written as a combination

relative power ratio such that the sum of twoof amplitudes depending on the frequency of thecomponents is kept constant:field, i.e., the Fourier component of the field, in𝐼tot = 𝐼1.6 eV + 𝐼3.2 eV ,contrast to simple power expansion within the1
𝐼𝑖 = 𝜖0 𝑐|𝐸𝑖 |2 , 𝑖 = 1.6 eV, 3.2 eV,
2perturbation theory. We need a theoreticaldescription

capableofnonperturbative

where 𝜖0 and 𝑐 are the vacuum permittivity andresponses to evaluate the response.

the

We have performed TDDFT simulations for

speed

of

light.

The

criteria

ofnonperturbative response based on the Keldysh

124

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

parameter [3] is 6.91 TW/cm2 and 27.2 TW/cm2

resonant excitation due to the 3.2 eV component.

for 1.6 eV and 3.2 eV. The absorbed energy is

When the lower frequency component does notevaluated by the total work from the field to theexist, 3.2 eV component cause Rabi oscillation

systembecause of the strong field. Rabi oscillation leads
to saturation of absorption in a pulse. By the∞𝐸abs = ∫ d𝑡 𝑱(𝑡) ⋅ 𝑬(𝑡)lower frequency driving for intraband motion, a0where 𝑱(𝑡) is the induced charge current

resonant point undergoes off-resonant condition

evaluated by TDDFT.

by the detuning. Then another off-resonant state

Our simulation shows maximal energy
absorption

isrealizedbyabecomesresonant. Thissequence

avoidscondition

saturation, and the number of excited electrons

𝐼1.6 eV ⁄𝐼3.2 eV ∼ 1.0 , when we have 𝐼tot =increases with the two-color mixture for the

1 TW⁄cm2 . This fact is hardly explained bydriving field.

perturbative expansion because the lower-ordernonlinear response is expected to dominate theReferences

dynamics for such weak intensity compared to[1] https://salmon-tddft.jp/, M. Noda, S.A. Sato,the atomic unit value of 35 PW/cm2. The

et al., Computer Physics Communications. 235,

perturbative expansion predicts 𝐼1.6 eV ⁄𝐼3.2 eV ∼356 (2019).

0.0 for the maximal absorption condition.

[2] Mizuki Tani, Kakeru Sasaki, Yasushi

This nontrivial maximal energy absorption

Shinohara, and Kenichi L. Ishikawa, Phys. Rev.

mixing ratio appears for other combinations (0.2

B 106, 195141 (2022).

eV, 3.2 eV), (0.8 eV, 3.2 eV). This generic[3] L. V. Keldysh, Sov. Phys. JETP 20, 1307

behavior is explained by energy detuning due to(1965).

the lower frequency components with the strong

125

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First-principles calculations of complex metallic alloy
surfaces
Kazuki NOZAWA
Department of Physics and Astronomy, Kagoshima University
1-21-35, Korimoto, Kagoshima 890-0065

High-resolution transmission electron microscopy experiments observed the formationof small ZnO patches on the PdZn(111) surface during the methanol steam reforming reaction, indicating the ZnO patches plays animportant role in the CO2 selectivity of thiscatalyst [1]. Last year we obtained an oxidized
PdZn(111) surface having a distorted hexagonal Zn-O structure at the top layer [2]. In thisfiscal year, we studied the dehydrogenation ofwater on this oxidized PdZn(111) surface. Calculations are performed with VASP program
package as flat MPI jobs on system B and C.
Because the obtained Zn-O layer on theoxidized PdZn(111) surface is corrugated asshown in the figure, first we calculated the potential energy surface (PES) of water with fixing atomic positions to find out promising candidates for the starting structure of the structural relaxation. We investigated the PES forwater with its molecular plane parallel and perpendicular to the surface, keeping the distanceto the nearest surface atom at 2.0 and 2.5 Å.
Many of these calculations were done as bulk
jobs on system B. Next, we performed structural relaxation from 20 stable starting structures obtained. We performed similar calculations for the hydroxyl and hydrogen on thesame surface to determine the stable coadsorption structure. The stable adsorption site ofwater was found on a Zn atom forming thehexagonal Zn-O ring. The obtained adsorptionenergy of water was larger than that on the
non-oxidized PdZn(111) surface but smaller

than that on ZnO(0001) surface [3]. However,the coadsorption energy of hydroxyl and hydrogen obtained in this calculation was larger
than that on the non-oxydized PdZn(111) surface [3]. It means this surface is not active forthe hydrogenation of water.

Figure 1: Side view of the oxidized PdZn(111)
surface.
In addition to the above work, we studied
pentacene adsorption on the twofold surface of
the icosahedral Ag-In-Yb quasicrystal [4] andthe electronic structure of trace (Pt, Ni, Pd)doped Al13 Fe4 intermetallic compounds.

References[1] M. Friedrich et al., Angew. Chem. 52,
4389 (2013).
[2] K. Iwamura, Master Thesis (Kagoshima
University, 2023).
[3] G. K. Smith et al., Surf. Sci. 605, 750(2011).
[4] M. Sato et al., J. Phys.:Conf. Ser., 2461,
012016(2023).

126

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Adsorption States of Oxygen on Pt Nanoparticles
Takehiko SASAKI and Yasumasa IMAI
Department of Complexity Science and Engineering, Graduate School of Frontier Sciences,
The University of Tokyo, Kashiwa-no-ha, Kashiwa, Chiba 277-8561
and contains 215 Pt atoms and oxygen atoms.

The surface state of the catalyst platinum
nanoparticles determines the performance ofAfterpolymer

cuboctahadral nanoparticle is obtained byelectrolyte

fuel

cells

(PEFC).

optimizationofoperation.

this
The

unit

cell,

Characterization of the electrochemical cycle

symmetry

structureand oxygen adsorption state on the surface of

optimization calculation was performed byplatinum nanoparticles, as well as the high-allowing the relaxation of the first and second

coverage

oxygenadsorption

state

with

neighbors of oxygen atoms. The energy cutoff

subsurface

oxygengeneration,

has

been

is 400 eV and the k-point mesh is 3x2x2. The

investigatedusingsynchrotron

representative models are shown in Fig. 1.

radiationtransient response XAFS and resonant inelastic
X-ray

scattering(RIXS).

Althoughcomputational studies on oxygen species on Pt
surfaces have been published [1,2], it isnecessary to determine the oxygen adsorption
structure on the Pt nanoparticles in order toFig. 1

conduct spectral analysis.

Pt586O64.

Obtained models Pt586O32 and

The Pt nanoparticles to be calculated are Pt
Nownanoparticles composed of 586 atoms of thecalculations

todetermine

cuboctahedron type corresponding to the actual

chemical potential for each species undercatalyst particle size range of 2.5 nm in order toelectrochemical processes are in progress.

study the high-coverage oxygen adsorption

References

state where subsurface oxygen atoms also exist.

[1] J.K. Norskov et al. , J. Phys. Chem. B 108,

The primary purpose is to obtain the adsorption

17886 (2004).

structure by VASP calculation and provide the[2] R. Jinnouchi et al., Catal. Today 262, 100

coordinates for FDMNES simulation. The unit

(2016).

cell has a volume of 22.13×44.26×33.19 (Å3)

127

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Modeling of the Ammonia Decomposition Reaction
on Iron-based Material Surfaces
Peijie FENG, Minhyeok LEE, and Yuji SUZUKI
Department of Mechanical Engineering, School of Engineering,
The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656
Ammonia is expected as a next-generation

barrier would significantly facilitate ammonia

green energy carrier due to its high hydrogen

decomposition. To estimate the reactivity ofdensity and ease of liquifying for storage andammonia on iron nitride and compare it with thattransportation. However, the direct application

on iron, DFT calculations are performed thatof ammonia as a fuel suffers from its low

illustrate the activation barrier of the rate-

flammability and potential NOx emission [1]. A

determining step on iron and iron nitride. Firstly,

fundamental understanding of ammonia reaction

the surface energy is calculated to identify the mostkinetics is necessary to overcome these issues.

common lattice surface where the reaction happens.

On the other hand, since ammonia-related

Secondly, the adsorption conformation of N2 andreaction kinetics has been developed andatomic N is calculated to obtain the system's total

validated

DeNOxenergy. Finally, the calculated conformation for theapplications, the kinetics model often loses its

reactant and product is used to estimate the totalrobustness in absence of oxygen, where thermalenergy of transition states. Based on a series ofdecomposition mainly occurs [2].

calculations, the activation energy can be estimatedmainlyfocusing

on

Therefore, we conducted density functionalfrom the energy difference between the transition

theory (DFT) calculations using the Vienna Ab

state and the initial reactant state.

initio Simulation Package (VASP) with the aid

The surface energy calculation on iron nitrideof the supercomputer system at ISSP to evaluate(FeN) is performed on lattice surfaces 100, 110

the rate-determining step of ammonia surface(with Fe or N at the top layer), and 111 (with Fe or

decomposition on iron-based materials.

N at the top layer). The result shows the lattice

It has been demonstrated that the nitrogensurface 110 has the lowest surface energy at aroundlargest0.086eV/Å2 as shown in Table 1. The lower theactivation energy on metal surfaces such as Fe,

surface energy, the more stable the surface is andCo, and Ni [3], and accordingly, it can be

the more common it is on the catalyst surface asrecombination

reaction

shows

the

assumed as the rate-determining step. Any surface
offering a lower N-N recombination activation

128

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

experimentally prepared. On the other hand, aclosed-packed surface (110) can be found on ironsurfaces, which usually possess the lowest surface
energy. Thus, FeN with lattice surface 110 and Fe
with lattice surface 110 are used to estimate andcompare the heterogeneous reaction of ammonia.
Table 1: Surface energy of iron nitride FeN.
Lattice
Surface

110

Top
Element-

Surface
Energy[eV/Å2]

0.086

100

Fe

N

Fe

111

Fig. 2: Initial and transition states of N-N
recombination for (a) Fe-110 and (b) FeN-110
surfaces.

Nnudged elastic band (CI-NEB) method [4]calculation can be performed first-principally,
which yields the transition states and the total

0.186 0.361 0.361 0.093

energies of the reaction between the given

Since N-N recombination is the key rate-

reactants and the products. As shown in Fig. 2, thedetermining step of ammonia decomposition, theFeN surface provides a lower activation energy

most stable adsorption site for N is found by(0.278 eV) than the Fe surface (2.41 eV),placing N atoms at all possible adsorption sites (asindicating a promising catalyst for ammonia

shown in Fig. 1) on the surface both for FeN-110

decomposition reaction.

and Fe-110. Among all possible adsorption sites,

Further improvements in finding the reaction

the Fe-top site on FeN-110 and the Hollow site on

kinetics shall be made through the harmonic

Fe-110 showed the lowest adsorption energy,transition state method.

indicating they are the most preferable active site

References

to adsorb free N atoms.

[1] M.V. Manna, P. Sabia, R. Ragucci, and M. de

By defining the product conformation for N2

Joannon, Fuel 264, 116768 (2020).

molecules on the surface, the climbing image[2] P. Glarborg, H. Hashemi, and P. Marshall,
Fuel Commun. 10, 100049 (2022).
[3] X. Duan et al., J. Mol. Catal. A Chem. 357, 81(2012) .
[4] G. Henkelman and H. Jónsson, J. Chem. Phys.
111, 7010 (1999).

Fig. 1: Adsorption site of (a) Fe-110 and (b) FeN110 surfaces.

129

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Study on the Catalytic Mechanism of RuTi Alloyin Catalyst-Referred Etching Method
Pho Van BUI, Daisetsu TOH, Kouji INAGAKI, Yoshitada MORIKAWA
Grad. School of Engineering,Osaka University, 2-1 Yamada-oka, Suita, Osaka 565-0871
Polishing is an extremely important techniqueformed and bonded to the catalyst surface.

used in the finishing processes for optical and

To improve the removal rate (RR) andsemiconductor surfaces and determines thestability, many catalyst metals have tested theirperformance of the final products. To produce

capability as a catalyst metal in CARE. Oursmoothany

recent studies found that Ni and Ru alloy are

crystallographically damaged surface, only

the best catalysts in terms of RR [4]. However,pure chemical etching method is desired.

the RR is decayed in time due to poisoning by

Recently, catalyst-referred etching (CARE)

removal products. Our recent experimental

method using Pt catalyst and water (as an

results have shown that alloying Ru with Ti

etchant) has been proposed [1]. CARE can

could effectively improve its stability forplanarize

crystalline

several hours of polishing compared to Ni or

semiconductor materials to atomically smooth

Ru itself [5]. The long-term stability of asurfaces [2,3]. The main role of the catalyst iscatalyst could reduce the cost and increase theto dissociate water molecules and to stabilize

reliability

the five-fold coordinated states. After a Si was

ultraprecision polishing method for next-

removed from the material, silicic acid isgeneration optical devices. Therefore, purpose

surface

SiC

withoutandintroducing

various

of

CARE

to

be

a

practical

Fig. 1: Top and side views of the Ru2M slab model with silicic acid. Adsorption energy of silicic acid.

130

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyoof the proposed research is to elucidate theshown in Fig. 1. The adsorption energy ofcatalytic mechanism of RuTi alloy and generalsilicic acid on RuTi alloy is the lowest among

understanding of the catalytic decay using first

the investigated catalysts. DFT calculations

principles calculations.

showed that Ti doping to Ru reduces the

The DFT calculations were performed using

adsorption energy of silicic acid on the Ru site

the Simulation Tool for Atom Technology

from 0.61 to 0.32 eV and efficiently restorespackage, which has clarified the adsorption andthe active Ru sites.

dissociative reaction on metal surfaces [6-8].

References

The electron–ion charge interactions were

[1] H. Hara, Y. Sano, H. Mimura, K. Arima, K.

described by an ultra-soft pseudopotential. The

Kubota, K. Yagi, J. Murata, and K. Yamauchi, J.

wavefunction

Electron Mater. 35, L11 (2006).

andchargedensitywere

expanded by a plane-wave basis set with 25 and[2] D. Toh, P.V. Bui, N. Kidani, S. Matsuyama,225

Y. Sano, Y. Morikawa, and K. Yamauchi, K.

Ry

cut-off

energies,

respectively.

Optimization was iteratively performed until

Rev. Sci. Instrum. 90, 045115 (2019).

the residual forces acting on all atoms at the[3] A. Isohashi, P.V. Bui, D. Toh, S. Matsuyama,saddle point were reduced to below 10−3 Eh/a0,

Y. Sano, K. Inagaki, Y. Morikawa, and K.

where Eh = 27.211 eV and a0 = 0.529 Å.

Yamauchi, Appl. Phys. Lett. 110, 201601

In this study, we used the 3×4×4 supercell(2017).

model of Ru with (0001) surface as the host[4] D. Toh, P.V. Bui, A. Isohashi, S. Matsuyama,

metal catalyst model, as shown in Fig. 1. A few

K. Yamauchi, and Y. Sano, Rev. Sci. Instrum.

Ru atoms are substituted by a transition metal

91, 045108 (2020).

atom, such as Zr, V, and Ti, creating a Ru-based[5] D. Toh, K. Kayao, P.V. Bui, K. Inagaki, Y.

alloy catalyst model. The adsorption energy,

Morikawa, K. Yamauchi, and Y. Sano, Pre. Eng.

Eadsorb, of silicic acid on Ru, Ru-V, Ru-Ti, andsubmitted.

Ru-Zr alloy catalyst surfaces was calculated by,[6] T. Hayashi, Y. Morikawa, and H. Nozoya, J.
Chem. Phys. 114, 7615 (2001).

𝐸𝑎𝑑𝑠𝑜𝑟𝑏 = |𝐸𝑅𝑢+𝑠𝑖𝑙𝑖𝑐𝑖𝑐 𝑎𝑐𝑖𝑑 − (𝐸𝑅𝑢 +𝐸𝑠𝑖𝑙𝑖𝑐𝑖𝑐 𝑎𝑐𝑖𝑑 )|,[7] Y. Hamamoto, I. Hamada, K. Inagaki, and Y.

where ERu+silicic acid, ERu, and Esicilic acid are theMorikawa, Phys. Rev. B 93, 245440 (2016).

total energies of the Ru-based catalyst with

[8] J. Quan, F. Muttaqien, T. Kondo, T.

adsorbed silicic acid, the Ru-based catalyst, andKozaeashi,

T.

Mogi,

silicic acid, respectively.

Hamamoto,

K.

Inagaki,

The adsorption energies of silicic acid on the

Morikawa, and J. Nakamura, Nature Chem. 11,

Ru catalyst and Ru-based alloy catalyst are

722 (2019).

131

T.

Imabayashi,

Y.

I.

Y.

Hamada,Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Proton Storage in Ruddlesden–Popper-type Sr3Fe2O7
Kosuke Kawai, Masashi Okubo
Department of Electrical Engineering and Bioscience, School of Advanced Science andEngineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555
We investigated proton storage properties of

performed in the same way. Graphics of crystal

Ruddlesden–Popper-type

structures were drawn by VESTA software.4

Sr3Fe2O7

using

density functional theory (DFT) calculations.

Three thermodynamically stable H+

This year, we focused on thermodynamicallysites were identified around interlayer

stable sites of proton in the lattice of Sr3Fe2O7.

space after structure optimization. Site A

DFT calculations were performed with theis bridged to the nearest O atom in the

Vienna ab initio simulation package (VASP).

same FeO6 octahedron, and the mostThe projected augmented wave method and athermodynamically stable. Site B in theplane basis set implemented in the VASP code

interlayer space is directed to an O atom

were utilized with a cutoff energy of 520 eV

in neighboring Fe2O7 layer. Site C is also

and k-point sampling on a 6×6×2

The

in the interlayer space but directed to a

generalized gradient approximation of theSr atom, resulting in high electrostatic

Perdew–Burke–Ernzerhof exchange-correlationenergy.

grid.1functional was used with Hubbard U = 4.0 eV

Referencesfor Fe to correct the self-interaction error of[1] G. Kresse and J. Furthmüller, Phys. Rev. B

correlated d electrons.2,3 An initial structure

54, 11169−11186 (1996).

model of Sr3Fe2O7 was obtained from theMaterials

Project

expanded

to a

(ID:

mp-18820),

superstructureof[2] J. P. Perdew, et al., Phys. Rev. Lett. 77,

and3865−3868 (1996).

2×2×1[3] F. Zhou, et al., Phys. Rev. B - Condens.

(Sr24Fe16O56, Fig.1a). Structure optimization

Matter Mater. Phys. 70, 1–8 (2004).

was performed with a convergence condition of[4] K. Momma and F. Izumi, J. Appl.

ΔF < 0.01 eV/Å. A H atom was located

Crystallogr. 41, 653−658 (2008).

manually near an O atom with a H–O distanceof < 1 Å, and then structure optimization was

Fig.1 (a) Crystal structure of Ruddlesden–Popper-type Sr3Fe2O7, (b) typical H+ sites, and (c) totalenergies of HSr24Fe16O56.

132

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Ultrafast adsorption states of two-dimensional metal-organicframeworks and semiconductors
Kenichi SHUDO
Fac. of Eng/Sci, Yokohama Nat’l Univ.
The University of Tokyo, Kashiwa-no-ha, Kashiwa, Chiba 277-8581
We performed calculation for elucidation of

motion was thus clarified.

a nonlinear optical effect of a typical transition

Metastable-atomelectron

spectroscopy

metal dichalcogenides, MoSe2, using Vienna ab(MAES) reveals electric states of a surface,initio Simulation Package (VASP). Molecular

exclusively giving information of the electron

dynamics produces well the atomic trajectories

cloud protrude into the vacuum. We measured

from an initial kick due to potential deformation

the spectra of such electrons of a typicalcausedHyperlinear

topological insulator, Bi2Se3. Our preliminary

components gives a sum frequency andcalculation of one to a few layers of this crystal,harmonics of fundamental oscillation (Fig.1).

using VASP. The obtained results are analyzed

These Fourier components are contrary to thebased on the local density of state dependent onlinear normal modes reachable either via

the distance from the top-most surface atoms.

experimental cw-Raman spectroscopy and

The spectral features of the MAES applied to thetheoretical diagonalization of atomic potential

crystal are concluded to be originated fromalong with the displacement of each atom. The

concurrent processes of resonance ionization

nonlinear optical effect derived by atomic

and Auger neutralization.

byphoto-transition.

Similar local electron distribution curves are
obtained also for layler-by-layer structures ofmetal-organic framework (MOF) of 5,10,15,20Tetrakis(4-carboxyphenyl)-porphyrin.

References[1] S. Bae, K. Matsumoto, H. Raebiger, K.
Shudo, Y.-H. Kim, Ø.S. Handegård, T. Nagao, M.

Fig. 1: Example of nonlinear vibronic

Kitajima, Y. Sakai, X. Zhang, R. Vajtai, P.

modes of MoSe2 simulated by means of

Ajayan, J. Kono, J. Takeda, I. Katayama:

molecular dynamics at femtosecond scale.

Nature Communications 13, 4279 (2022)

See Ref [1] for detail.

133

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Analyzing Reaction Mechanism of Charge Transfer inCatalysis by Electronic Structure Calculations
Tatsuya JOUTSUKA
Institute of Quantum Beam Science, Graduate School of Science and Engineering, Ibaraki
University, Hitachi, 316-8511 Ibaraki, Japan.

Frontier Research Center for Applied Atomic Sciences, Ibaraki University, 162-1
Shirakata, Tokai, Ibaraki 319-1106, Japan.
Carbon Recycle Energy Research Center, Ibaraki University, Hitachi, 316-8511 Ibaraki,
Japan.
Focusing on the selectivity of zirconia-

suggesting that Zn species are unevenly

catalyzed methanol synthesis, we calculated

distributed on the surface and deposited nearadsorption and reaction energies to clarify thethe surface; by adding excess Zn to ZrO2, bothreaction mechanism. [1-2] In addition, polaronZn-containing clusters and ZnO nanoparticles

transfer rate constants in bulk titanium dioxide

were formed. We also analyzed the dynamics in(TiO2) and SrTiO3 were calculated by theadsorption and chemical reactions by molecularconstrained density functional theory (CDFT)

dynamics simulation and develop a method tomethod.

Then,

the

analyze charge transfer. In addition, we

calculation

was

used

electronic
tostructureelucidatetheelucidated the mechanism of Cu nanoparticle

photocatalytic reaction mechanism and crystal

formation with a size <10 nm using DFT

plane selectivity in photocatalysts.

calculations.[3]Furthermore,

weanalyzed

A zirconia-based solid solution catalyst

theoretical methods for proton transfer in liquid

ZnZrOx was prepared by substituting Zn withwater,[4] cross-coupling of arylboronic acidZr, which has high catalytic activity. The

and imidazole on t-ZrO2,[5] and Cu deposition

correlation between the binding strength of theby chemical vapor deposition using a CuI

substituted elements and adsorbates (reactants,

precursor[6] using VASP and cp2k program

products, and intermediates) and methanol

packages.

synthesis capacity was clarified. The DFT

In addition, the polaron transfer rate

calculations using cp2k program package

constants in bulk TiO2 and SrTiO3 (Fig. 1) was

showed that the Zn species in the clusters arecalculated by the CDFT method along themore likely to be exposed on the surface. This

several directions (such as [101] direction in

result is consistent with experimental results,

TiO2) using cp2k program package. We found134

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

that the CDFT method can evaluate the

Kinoshita,

Mitsuhiro

Yoshida,Natsumipotential energy profile more accurately than

Shimada,TatsuyaJoutsuka,

Masahikolinear interpolation of structures that has been

Nishijima, Tetsuo Honma, Noriko Yamauchi,frequently employed for bulk TiO2. We next

Yoshio Kobayashi, and Kenta Iyoki, ACS

moved on to the SrTiO3 (001) and (110)

Catal., 12, 7748–7759 (2022).

surfaces to understand the porlaron transfer at[2] Tatsuya Joutsuka and Shohei Tada,* J. Phys.

the surface. We are now studying the relation

Chem. C, 127, 6998–7008 (2023)

with the oxygen vacancy and electron polaron[3] Tatsuya Joutsuka, Ryu Hamamura, Kakeru

to discuss the photocatalytic mechanism further

Fujiwara, Tetsuo Honma, Masahiko Nishijima,using cp2k program package.

and Shohei Tada, Int. J. Hydrog. Energy, 47,
21369–21374 (2022).
[4] Tatsuya Joutsuka, J. Phys. Chem. B, 126, 24,4565–4571 (2022).
[5] Masaru Kondo, Tatsuya Joutsuka, Kakeru
Fujiwara, Tetsuo Honma, and Shohei Tada,
Catal. Sci. Technol., 13, 2247-2254 (2023).
[6] Gento Toyoda, Hikari Kikuchi, Satoshi
Yamauchi, Tatsuya Joutsuka, Takashi Fuse, andYusuke Kubota, Jpn. J. Appl. Phys., 62,

Fig. 1: Bulk SrTiO3.

SH1002 (2023).

References[1] Shohei Tada, Nagomu Ochiai, Hiroka

135

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First-principles calculation of oxygen defects on the surfaceof titanium dioxide TiO2
Shoya Kawano
Department of Electrical and Electronic Engineering, Kyushu Institute of Technology,
Kitakyushu, Fukuoka, 804-8550
We have studied stability of oxygen defects
on

TiO2

surfacebyfunctionalthem from the PseudoDojo [3]. The exchange-

calculations in this project. Oxygen defects incorrelation function is used Perdew-Burke-

TiO2

catalytic

Ernzerhof type. For example, Γ-point-centeredperformance, carrier transport, hydrophilicity,

2×2×1 k-points sampling were used for 2×2×4

and other functions. Therefore, if we cansurface unit cell of TiO2 (110) included 238

successfully control these oxygen defects, it

atoms. We use the energy cutoff of 96 Ry forwill be possible to design the function of TiO2.

the wave function, a convergence criterion for

For this purpose, we have studied thethe electronic self-consistency loop of 10−10 Ry,

configuration of oxygen defects on TiO2

and the Gaussian smearing method with asurface using scanning probe microscopy

smearing width of 0.002 Ry. Lattice parameters(SPM) image with template matching method.

of TiO2 is referred to previous report [4].

significantly

density

Vanderbilt PSeudopotential), and are obtainedchanges

its

This SPM image analysis indicated typicalFigure 1 shows calculated structure of TiO2

configuration of oxygen defects. Therefore, we

surface model of 2×2×4 surface unit cell withinvestigated

two defects (238 atoms) and their defect energy

stabilityof

configurationofEvac. Evac is defined as follows

oxygen defects on TiO2 surface by density

𝐸𝑣𝑎𝑐 = 𝐸𝑇𝑖𝑛 𝑂2𝑛−1 + 𝐸𝑂2 − 𝐸𝑇𝑖𝑛 𝑂2𝑛 ,functional calculations.
functional

where 𝐸𝑇𝑖𝑛 𝑂2𝑛−1 , 𝐸𝑂2 and 𝐸𝑇𝑖𝑛 𝑂2𝑛 are total

calculations for slab models of rutile TiO2 (110)energy of slab with defects, total energy ofby using Quantum ESPRESSO code [1,2]. The

oxygen molecule and total energy of slab

slab models have two oxygen defects on

without

surface and 1 nm vacuum gaps. The unit cellconfigurations for two defects introduced atincluded 144, 240, 300 and 450 atoms arebridge oxygen on the surface. The bridge

considered for slab models without defects. In

oxygen is bonded with titanium along the

Quantum ESPRESSO, we use the norm-

parallel axis in Fig. 1. Configurations of defects

conserving pseudo potentials generated by thelike Fig. 1 (a)-(c) are more stable than defects

code ONCVPSP (Optimized Norm Conserving

along parallel axis in Fig.1 (d) and (e). ThisWe

performed

density136

defects.

We

consideredfiveActivity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

trend of results is consistent with our STM[2] P. Giannozzi et. al. J. Phys. Condens. Matter.

image analysis.

29, 465901 (2017)

We have used the L4cpu of system B with

[3] M. J. van Setten, M. Giantomassi, E.

512 parallel computing for 144 and 240 atoms

Bousquet, M. J. Verstraete, D. R. Hamann, X.

unit cell, the L16cpu of system B with 2048

Gonze, G.-M. Rignanese, Computer Physics

parallel computing for 300 and 450 atoms unit

Communications 226, 39-54 (2018)cell.

[4] X. W. Wang, B. H. Zhang, L. Y. Sun, W. N.
Qiao, Y. D. Hao, Y. C. Hu, and X. E. Wang,

References

Journal of Alloys and Compounds 745, 856[1] P. Giannozzi et. al. J. Phys. Condens. Matter.

(2018).

21, 395502 (2009).

Fig. 1 Calculated structure of TiO2 surface model and their defect energy.

137

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Thermal conductivity calculation for high thermal conductive
insulator
Shoya Kawano
Department of Electrical and Electronic Engineering, Kyushu Institute of Technology,
Kitakyushu, Fukuoka, 804-8550
We have computed the lattice thermal

Ernzerhof type exchange-correlation function.

conductivity of several materials to create aFor

finitedisplacement

andrandomdatabase for the design of high thermal

displacement

approaches,

we

used

conductive

displacement distances of 0.02 and 0.04 Å,

insulators.

High

thermalconductivity insulators are essential for therespectively.

substrate of power devices. To develop high-We

calculatedthelatticethermalperformance power devices, it is necessary to

conductivity of AlAs, CaO, SrO, YN, CeO2,

discover new substrate materials with high

AlN, CaF2, MgBe2N2, TaGaFe2, and BN. Thesethermal conductivity insulators. In this project,structures were collected from the Materials

we created a database of lattice thermal

Project [5]. For the random displacement

conductivity for materials informatics.

approach, we used 20-500 structures andWe performed density functional phononcompared their thermal conductivity accuracy.

calculations using the ALAMODE code [1]We found that a set of 20 structures satisfied thewith the Quantum ESPRESSO code [2,3] toaccuracy for the lattice thermal conductivity

compute the lattice thermal conductivity. We

database. We continue to calculate the latticecalculated the second- and third-order force

thermalconstants from density functional theory. The

parameters in this project to create a database.

conductivity

with

the

obtainedsecond-order force constants were calculated

We used the F16CPU of system B withusing the finite displacement method, while the2048 parallel computing by MPI for the density

third-order force constants were calculated

functional calculations for each displacement.

using

For

the

randomdisplacement

methodthermalconductivity

calculation,

implemented in ALAMODE. In the density

MPI/OpenMP hybrid parallelization was usedfunctionalwith the F16CPU of system B.

calculations,

we

used

norm-

conserving pseudo-potentials generated by the
Optimized

Norm

Pseudopotential

Conserving
code,

References

Vanderbilt

obtainedfrom[1] T. Tadano, Y. Gohda, and S. Tsuneyuki, J.
Phys.: Condens. Matter 26, 225402 (2014)

PseudoDojo [4]. We used the Perdew-Burke-138

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo[2] P. Giannozzi et. al. J. Phys. Condens. Matter.
21, 395502 (2009).
[3] P. Giannozzi et. al. J. Phys. Condens. Matter.
29, 465901 (2017)[4] M. J. van Setten, M. Giantomassi, E.
Bousquet, M. J. Verstraete, D. R. Hamann, X.
Gonze, G.-M. Rignanese, Computer Physics
Communications 226, 39-54 (2018)[5] https://materialsproject.org/

139

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Stable structures of low-dimensional systems fromﬁrst-principles
Shota ONO
Department of Electrical, Electronic and Computer Engineering, Gifu University
Gifu 501-1193

In this project, we have studied (i) stable structures of two-dimensional (2D) hexagonal materials [1], (ii) structural and electronic
properties of 2D ionic crystals [2], and (iii) surface rippling on non-close-packed metal surfaces [3] using density functional theory (DFT)and machine-learning approaches.
For the case (i), we have explored the stable
conﬁgurations of atomic species on a hexagonal plane in 2D binary systems. The conﬁguration search was accelerated by using theBayesian optimization (BO) approach. For the
2D copper-based systems of Cu-X, we haveshown that the structures having a hexagonal or elongated ring of Cu (or X) atoms containing a X (or Cu) cluster in the unit cellare relatively stable, in particular, for X =Be, Pd, and Au (see Fig. 1(a)). The binary
Lennard-Jones model was used to interpret the
optimized structures predicted by DFT calculations.
For the case (ii), we have shown that 4 and
12 IA-VII alkali halides in the 2D hexagonal and tetragonal structures (see Fig. 1(b))
are dynamically stable, respectively. The electronic band gaps range from 6.8 eV for LiF to
3.9 eV for RbI and CsI in the tetragonal structure within the generalized gradient approximation. A hard sphere model that considers
the Madelung energy and the core-core repulsion explains the nearest-neighbor bond lengthand the cohesive energy of 2D alkali halides.
For the case (iii), we have studied surface
rippling of X/A(100) (see Fig. 1(c)), assuming

X = H to Bi except for noble gases and A =Cu, Ag, and Au. We have shown that small
atoms (such as H, C, N, O and F) attract electrons from the substrate due to the large electronegativity, which prevent them from passing through voids in the (100) surface. We
have also discussed a breakdown of the hard
sphere model for predicting the surface rippling amplitude.

Figure 1: Stable structures (a), (b), and (c)studied in Refs [1], [2], and [3], respectively.
The structures in (a) were found by using BO.

References[1] S. Ono: AIP Advances 12, 085313 (2022).
[2] S. Ono: J. Phys. Soc. Jpn. 91, 094606(2022).
[3] S. Ono, H. Satomi, and J. Yuhara: Comp.
Mater. Sci. 218, 111959 (2023).

140

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Stable structure of vacancy cluster in tungsten and irontrapping multiple hydrogen atoms
Kazuhito Ohsawa
Institute for Applied Mechanics, Kyushu University,
Kasuga-koen, Kasuga, Fukuoka 816-8580

Introduction

Simulation method

Tungsten (W) and Iron (Fe) are important

Stablevacancyclusterstructuresandmaterials for plasma facing materials (PFMs)

positions of multiple H atoms trapped in theused in fusion reactors. In particular, W shows

vacancy clusters in W and Fe specimen werepromise as divertor armor tiles exposed to thecalculated

most

irradiation.

calculations based on density functional theory

Vacancy (V) and vacancy clusters nucleated in(DFT). We used the Vienna ab-initio simulation

the metals under the irradiation circumstancepackage (VASP) for the DFT simulations. A

are considered to be a large amount oflarge simulation cell, composed of 5x5x5 bcc

hydrogen (H) and hydrogen isotope trap sites.

lattice (250 atoms), were used in order to

In particular, tritium (T) retention in the PFMs

reduce the effects of periodic boundary

is a serious problem for safety operation of

condition imposed on the simulation cell. The

fusion reactor because T is radio isotope whose

cut-off energy of plane wave was 350eV.

intenseplasmaparticle

in

terms

of

first-principle

The binding energy Eb of di-vacancy V2 washalf-life is about 12 years. So, the investigation

defined asof the vacancy clusters in the metals and H

Eb(V2) = 2E[V]-E[V2]-Eref,trapping are important subjects in the field of(1)

PFMs. In the present work, stable structures ofwhere E is cohesive energy of the supercell andvacancy clusters and their interaction withEref indicates energy of perfect crystal. For

multiple H atoms in W and Fe lattice areexample, E[V] and E[V2] mean cohesive

investigated by computational methods. A

energies of supercells containing mono- and di-difference of vacancy clusters in W and Fe wasvacancy, respectively. Positive binding energy

clarified. Besides, positions of multiple H

Eb corresponds to attractive interaction of theatoms trapped in the vacancy type lattice

two vacancies. Similarly, the binding energy ofdefects are examined.

V-H complex V2H2 i.e., di-vacancy trapping

The effects of H were

two H atoms, is defined as

investigated on the stable structures of theEb(V2H2) = 2E[VH]-E[V2H2]-Eref,.

vacancy clusters.

141

(2)

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Results and discussion
Stable structures of di-vacancy in W and Fe
are different, as shown in Fig.1.

W is <111> arrangement in both cases. Figure 3

The most

shows two H atoms trapped in di-vacancy in W

stable arrangements of two vacancies are <111>

and Fe. H atoms trapped in the vacancy type

in W and <100> in Fe, respectively. Actually,lattice defects are allocated in the vicinity ofbinding energy between two vacancies arranged

octahedral interstitial (O-site). In addition to it,

in <100> direction is larger than that of <111>

H atoms preferentially occupy O-sites locatedin Fe estimated in Eq. (1), according to Fig. 2.

in the center of the di-vacancy where two

However, <111> arrangement of di-vacancy

vacancies are in contact with each other.

trapping two H are more stable than <100> one

In the present work, H trapping are supposed

estimated in Eq. (2). Therefore, stable structure

to play important roles for stabilization ofof di-vacancy in Fe is changed from <100> tovacancy clusters. The binding energy of<111> arrangement by the H trapping. By thevacancy clusters increase and structure of di-way, the most stable structure of di-vacancy invacancy are changed by the H trapping in Fe.

Fig. 1: Most stable structure of di-vacancy in bcc lattice. Arrangements oftwo vacancies are (a) <111> in W and (b) <100> in Fe, respectively.

Fig. 2: Binding energy of di-vacancy andFig. 3: Positions of two H atoms trapped in

di-vacancy trapping two H atoms in Fe.

di-vacancy in W and Fe.

142

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Ab initio molecular-dynamics study of structural and transport
properties of liquid mixtures containing hydrocarbon underultra-high pressure
Satoshi OHMURA
Department of Civil and Environmental Engineering,
Hiroshima Institute of Technology, Saeki-ku, Hiroshima 731-5193
Understanding the structures and physicalthe first peak are highly stable and strong. The

properties of the C-H system at higher pressuressimulation found that the interaction remains

is essential to constrain the interiors of the ice

even under ultrahigh pressure conditions of 1300

giant planets (extrasolar planet). Therefore, it isGPa. For hydrogen, molecular hydrogen isimportant for planetary science to understand theobserved after methane dissociation (at 150

behavior of this important material in extreme

GPa). When the pressure becomes higher,

conditions. For this reason, we have performedhydrogen molecule becomes unstable.

ab initio molecular-dynamics (MD) simulations
for liquid Methane (CH4) under high pressures.
We used a 500-atom system consisting of 400
hydrogen and 100 carbon atoms in a cubic
supercell with periodic boundary conditions.
Figure 1 shows the pressure dependence ofthe pair distribution functions gαβ(r) of liquid
CH4 obtained from the MD simulation. Since
methane molecules remain intact up to 100 GPa,
the shape of gCC(r), gCH(r) and, gHH(r) at 55 and100 GPa are almost the same. Above 150 GPa,in gCC(r), a new peak appears at approximately
1.4 Å and its position gradually shifts to asmaller r with increasing pressure. Moreover,Fig. 1: Pressure dependence of the pair distribution

the first peak of gCC(r) is clearly separated fromfunctions gαβ(r) of liquid CH4.

a second peak at pressures of 150 GPa,suggesting that the C-C bonds contributing to143

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Temperature dependence of structural and electronic
properties of organic molecular thin film surfaces
Kaori Niki
Graduate school of science
Chiba University, Yayoi-cho, Inage-ku, Chiba 263-8522
In recent years, it has been reported that

The Generalized Gradient Approximation

organic molecules' electronic and physical(GGA) within the Perdew-Burke-Ernzerhof

properties interact with the surface using Wave

(PBE) formalism described the exchange-

numberPhotoelectron

correlation effects. We used a unit cell with

Spectroscopy. We have investigated the copper

lattice constants a = b = 3.53 Å, c = 20.0 Å, α(Ⅱ) phthalocyanine (CuPc) - monolayer= β = 90° and γ = 120°, and plane-wave basis

substrate system. This year, we investigated

set with the projector augmented wave (PAW)

molecular motion depending on the terminal.

approach with an energy cutoff of 380 eV. The

Monolayer TiSe2 has two typical phases

Brillouin zone integration was performed on adepending on the temperature. A (1 × 1)

Monkhorst-Pack of 18×18×1 grid of k-points.

structure (normal phase) is shown at room

The energy convergence criterion chosen fortemperature, and a (2 × 2) superstructure

the self-consistency cycle was 1×10-7 eV. To(CDW phase) is realized under the 200 K.

account

Each phase has a band gap of 83 meV and 153

temperature parameter σ for Methfessel-

meV, respectively [1, 2]. We have adopted

Paxton smearing was applied [5]. So far, we

this interesting thin film as the substrate andhave found the parameter set (U = 3.9 eV andreported the structural changes depending on

σ = 0.01 eV), which reproduces thethe temperature in the previous year andexperimental result [6]. We used thesestructureparameters for the MD simulation of the CuPc

space-resolved

optimization,

resulting

in

an

for

Fermi

surfacebroadening,

adsorption distance of 3.16 Å.

/ TiSe2 structure. After relaxing the geometries

This time, we performed Molecular dynamics

at 0 K, the systems were brought up to 654 K(MD) simulation using these structures as theby repeated velocity rescaling using the spin-

ground state, and clarified the structures after

polarized PBE+U DFT-D3 functional at the Γ-

temperature change.

point. Then 5

MD calcuration of CuPc on TiSe2

trajectories were generated with a 1 fs time

We performed density functional theory

step.

(DFT) calculations using the Vienna Ab Initio

Molecural rotation of CuPc/TiSe2

Software Package (VASP) version 5.4.4 [3,4].

ps microcanonical MD

Figure 1 shows the geometry of CuPc on

144

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

at lower temperatures.

TiSe2 at 0 K. S1 and S2 indicate the axis of thesurface. “Center, " one of the molecular axes,

On the other hand, after MD calculation, thenumber of Se electrons in TiSe2 is slightly

is tilted 30 degrees from the axis s2.

larger than before calculation. On the other
hand, the number of electrons in Cu atom
decreases. A stable structure is due to the close
distance between Cu and Ti creates. Nitrogencloser to the center has additional electrons andapproaches Ti atoms. This also creates a stablestructure.
Figure 1. Geometry of CuPc/TiSe2

Conclusion
Figure 2 shows the geometry of CuPc/TiSe2

By performing MD calculations using VASP,

after MD. A temperature of 654 K is assumed.

we clarified changes in the charge distribution

We can see the motion of the molecule to theof molecules and substrates and molecular

lower left. The angle between the central andmotions with increasing temperature.

s2 is magnified to 60 degrees.

References

After MD

[1] J.-P. Peng et. al., Phys. Rev. B 91, 121113(2015).
[2] P. Chen et. al., Nat. Commun 6, 8943
(2015).
[3] G. Kresse and J. Furthmuller, Phys. Rev. B,
54, 11169 (1996).
[4] G. Kresse and J. Hafner, Phys. Rev. B, 49
14251 (1994).
[5] M. Methfessel and A. T. Paxton, Phys. Rev.
B 40, 3616 (1989).

Figure 2. Geometry of CuPc/TiSe2[6] R. Bianco et.al., Phys. Rev. B 92, 094107
Cu is adsorbed between Ti and Se atoms(2015).

before MD simulation. At this time, charge
repulsion occurs between Cu and surrounding
Se, and between carbon and Se near the center

145

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Development of First Principles methods for Light-Matter
Interaction
Hiroki KATOW
Photon Science Center, Graduate School of Engineering,
The University of Tokyo, Bunkyo-ku, Tokyo 113-8656, JapanThe electronic dipole moment calculation isstates of monolayer (ML) and bulk hexagonala fundamental of the light-matter interaction. In

boron nitride (hBN) for further systematic study.

isolated systems, the dipole moment is defined

Massively parallelized calculations using 2048

by an expectation value of dipole operator and

cpu in F16cpu class were conducted. We show

no ambiguities in its definition. Contrarily inthe exciton energy spectrum of ML- and bulk-

periodic systems, ambiguity of the choice of

hBN in Figure 1. Due to the weak screening ofcell boundary leads to the indefiniteness of its

two-dimensional system ML-hBN possesses

definition. Modern understanding of this issue

larger GW gap (8.12 eV) than bulk-hBN (5.54

is that the ambiguity is rooted in the indefiniteeV). We also confirmed huge exciton binding

complex phase factor of one-particle orbitals,

energy of ML-hBN. As a future prospect, we

which is called gauge degrees of freedom. A

apply our gauge-free method to ML- and bulk-

solution is proposed for ground states by King-hBN by utilizing the excitonic wave functions

Smith and Vanderbilt where the dipole moment

computed in this project.

is reformulated to exclude explicit dependency
on the gauge [1].
A same obstacle is present in excitonic
systems. The author of this report andcolleagues have developed a gauge-free method
for excitonic systems [2] as an extension ofKing-Smith and Vanderbilt’s formula. We
demonstrated the validity of our gauge-freeFig. 1 Exciton energy spectrum of thedipole calculation of Graphane exciton by using

monolayer hBN (left) and bulk hBN (right)

System B. We employed Quantum ESPRESSO

calculated by using BerkeleyGW package [4][3] and BerkeleyGW [4] package to computewhich is based on GW+BSE method. Red lines

excitonic wave functions based on GW+BSE

indicate GW gap.

method. In this project we calculated excitonic

146

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

047401 (2022).

References[3] P. Giannozzi et al., J. Phys. Condens.

[1] R.D. King-Smith et al., Phys. Rev. B

Matter 21, 395502 (2009).

47, 1651(R) (1993).

[4] J. Deslippe et al., Comput. Phys.

[2] H. Katow et al., Phys. Rev. Lett. 129,

Commun. 183, 1269 (2012).

Fig. 1: Supercomputer System B at SCC-ISSP.

147

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Study on structural elementary excitations at
semiconductor surfaces and interfaces
Hiroyuki KAGESHIMA
Graduate School of Natural Science and Technology, Shimane University
1060 Nishi-Kawatsucho, Matsue, Shimane 690-8504

In this project, we have been focused on
physical properties of structural elementary
excitations of semiconductor surfaces and interfaces. In this year, we have focused on
the physical properties of SiO self-interstitialin SiO2 . [1] The calculations were performed
based on the ﬁrst-principles calculation. Program package PHASE/0 was employed. [2]When Si is oxidized, a large volume expansion occurs at the interface, so Si is released
into the oxide ﬁlm in order to relax the generated strain. This is because a slightly denser
and Si-rich region is formed near the interface,and a concentration gradient of excess Si isgenerated, which tends to promote the migration and diﬀusion of excess Si toward the oxide
ﬁlm side. Moreover, the presence of excess Si
is thought to induce and to enhance structuraldeformation of the oxide ﬁlm. Therefore, thebehavior of excess Si in the near-interface oxide
ﬁlm governs strain relaxation in two ways. In
this study, we traced the path of the migrationand diﬀusion of excess Si at the atomic level,
and investigated the energy proﬁle by the ﬁrstprinciples calculations.
A quartz/Si(100) interface model with four
Si layers and four SiO2 layers was used for the
calculation. The front and back surfaces are
terminated by H, and superslab geometry witha suﬃcient vacuum layer is prepared. The in√√plane periodicity is 2 2 × 2 2, which is suﬃciently large. The unit cell contains 142 atoms.
We used plane wave bases with the cutoﬀ of
30 Ry, and norm-conserving pseudopotentials

for Si and H as well as an ultrasoft pseudopotential for O. We also used the exchangecorrelation density functional of generalized
gradient approximation (GGAPBE). We employed the Monkhorst-Pack k-point samplingof 4×4×1. Using the structure with interstitial
SiO placed at the interface as the initial structure (Fig. 1), we searched for migration diﬀusion paths. In the route search, the structure
optimization calculation was performed by ﬁxing one Si layer and H on the back surface. For
comparison, a SiO2 crystal model equivalent to
one in which only the oxide ﬁlm was taken out
and arranged periodically was also used to investigate the characteristics of the interface.
We assumed that the excess Si is interstitial
SiO in the oxide ﬁlm, and that migration diffusion consists of three elementary processes.
These are O vacancy transfer, Si coordination
number conversion, and Si bond order conversion. All of these elementary processes are effective only for migration diﬀusion of interstitial SiO, and are not considered for other formsof excess Si. In particular, O vacancy transfer
and Si coordination number conversion are essential. In order for Si coordination number
conversion to occur, O vacancy transfer must
be repeated to move O vacancies to appropriate positions. Si bond order conversion can besubstituted by these two, but it has the eﬀect
of shortening several steps at once.
Based on these assumptions, we succeeded inconstructing a transport diﬀusion path of theexcess Si from the interface to the oxide ﬁlm

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Figure 1: Starting atomic structure for the migration diﬀusion path of the SiO interstitial.
Large, middle, and small spheres indicate Si,
O, and H atoms, respectively.
surface. Moreover, the energy proﬁle is numerically consistent with previous experimental results. It was also found that near theinterface, migration diﬀusion is diﬃcult due tostrong structural constraint from the Si crystal side. The highest energy is 2.37 eV for
the initial structure, and the energy drops to−1.47 eV when interstitial SiO reaches the surface. This diﬀusion path we found consists of
10 steps of O vacancy transfer, 15 steps of Si
coordination number conversion, and 1 step of
Si bond order conversion. It should be notedthat Si and O belonging to SiO interstitaial at
the ﬁnal structure is diﬀerent from those at thestarting structure. SiO interstitial moves withsequentially kicking the neighboring Si and O
like a billiards, and the kicked-out Si and O
become new interstitial SiO, while Si and O
belonging to the old intersititial SiO are absorbed as a part of the oxide.
So far, we have reported that, by the ﬁrstprinciples molecular dynamics method, the migration and diﬀusion of atoms in Si-rich SiO2 isenhanced under compressive strain, [3] and by
the ﬁrst-principles nudged-elastic-band (NEB)

method, the migration and diﬀusion of the O
vacancies in SiO2 is conversely reduced. [4]Therefore, we have speculated that the movement and diﬀusion of atoms in SiO2 , which
has a large Si composition, is governed not by
the movement and diﬀusion of O vacancies butby the Si coordination number conversion due
to interstitial SiO. Our results are numericallyconsistent with these calculation results, which
supports our speculation, and suggests the importance of the behavior of interstitial SiO in
Si oxidation.
In this calculation, the barrier height ineach elementary process is not calculated yet.
For this reason, the comparison of our resultswith the experimental results and our previous computational results is unfortunately
still insuﬃcient. We are planning to calculate barrier heights by using the ﬁrst-principles
NEB method, and to compare numerical values much more in detail.

References[1] H. Kageshima, T. Akiyama, and K. Shiraishi, Matter. Sci. Semicon. Process. 162(2023) 107527 (7 pages).
[2] https://azuma.nims.go.jp[3] H. Kageshima, Y. Yajima, K. Shiraishi,
and T. Endoh, Jpn. J. Appl. Phys. 58(2019) 111004 (11 pages).
[4] K. Yata and H. Kageshima, Jpn. J. Appl.
Phys., 60 (2021) 035504 (6 pages).

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Exponent puzzle for metal-insulator transition in
doped semiconductors
Yosuke HARASHIMADivision of Materials Science, Nara Institute of Science and Technology
Takayama-cho, Ikoma, Nara 630-0192

The zero temperature conductivity is zero at
low impurity concentrations, and it becomes ﬁnite at high impurity concentrations. A metalinsulator transition occurs at concentrations in
between. This metal-insulator transition is a
general phenomenon of quantum transport andis known to occur in many semiconductors.
Understanding the mechanism of the metalinsulator transition leads to an understandingof the general theory of quantum transport.
Here, electrons are scattered by randomly
conﬁgurated impurities, and the phases ofwavefunctions are randomly shifted out. Those
waves interfere each other and propagation is
inhibited, resulting in the appearance of localized states. This is called Anderson localization and a metal-insulator transition driven bydisorder is called Anderson transition. Anderson transition is a continuous transition, andthe correlation length ξ diverges at the transition point as ∝ |n − nc |−ν . The critical exponent, ν, which characterizes this divergence,
plays an important role in understanding thephysical characteristics of the system. Since
the critical exponent is universal and independent of the details of the system, the criticalexponent can be used to classify systems. In
other words, when two systems have diﬀerent
critical exponents, it means that they have different intrinsic physical features and are completely diﬀerent physical phenomena.
Although there have been several experimental reports on the critical exponent of themetal-insulator transition, the proposed mech-anism has not yet been clariﬁed. There are
two values of ν ≃ 0.5 and ≃ 1.0–1.3 that havebeen reported experimentally. This problem
is called the ”exponent puzzle” and the origin of descripancy has been studied for longtime. One possible origin is the eﬀect of carrier compensation due to unintentionally introduced impurities or lattice defects, but this has
not been conﬁrmed theoretically. The purposeof this study is to elucidate the exponent puzzle by conducting a theoretical analysis considering the compensation eﬀect in order to clarify
the essential factor that causes the descripancy
in the critical exponent.
Figure 1 is a calculated electronic density ina randomly distributed impurity system. The
calculation is based on the density functional
theory and local density approximation. We
performed electronic structure calculations ofcompensated systems with spin conﬁgurations
by using a program code used in our previous
work. Positive ions and negative ions are randomly distributed in three dimensional space.
They represent donors and acceptors. We solve
the electronic states such that charge neutrality is satisﬁed, i.e., the number of electrons isequal to the diﬀerence between the number ofdonors and acceptors.
We also have examined stable impurity conﬁgurations in doped systems. The results are
opened in Refs. [1, 2].

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Figure 1: Total electronic density (left panel) and spin density (right panel) in a three dimensional system. Donor and acceptor ions are shown as blue and green points, respectively. In theright panel, positive and negative values denote up and down spin, respectively. The number ofdonor ions is 100, and that of acceptor ions is 50.

References[1] Y. Harashima,
et. al.:IEEE
Trans.
Semicon.
Man.,
accepted,
DOI:10.1109/TSM.2023.3265658.
[2] Y. Harashima, et. al.: arXiv:2303.14891.

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Phase stability of high-entropy alloys
Yoko Yamabe-Mitarai
Frontier Science Graduate School,
The University of Tokyo, Kashiwa-no-ha, Kashiwa, Chiba 277-8561
comprise

Special Quasirandom Structure was used tomultiple elements with near-atomic composition

simulate random atomic position for ternary

and have high configurational entropy ofalloys as shown in Fig. 1. The internal

constituent elements. HEAs have been attracted

coordinates was optimized using Quantum

as new generation structural and functional

Expresso (QE). The total energy and density ofmaterials since this new paradigm was proposed

statesin 2004 [1]. Most of HEAs have a single face-coordinates. The formation energy calculated

center-cubic (fcc), body-center-cubic (bcc)from the total energy indicated Al, Sn, Hf and Zr

structures or their mixtures. Due to limited slip

stabilized hcp structure, while Nb and Mo

system, hexagonal-close-pack (hcp) structure isstabilized bcc structure.

High-entropy

alloys(HEAs)

wascalculated

on

the

optimized

expected to be high temperature materials. Largesolid-solution hardening is also expected for
HEAs. Then, we are applying to find HEAs withhcp structure experimentally and by using DFT
calculation. Ti is focused on one of constituent
element because Ti is used as high-temperaturematerials due to its small density and highspecific strength. Phase stability of alloys wasFig. 1:BCC (top) and HCP (bottom) SQS model.

experimentally investigated, and a few alloys

X is alloying elements.

were found to be a hcp structure [2]. To

References

investigate the effect of alloying elements and[1] B. Cantor, I. T. H. Chang, P. Knight, A. J. B.

composition dependence on phase stability,

Vincent, Mater. Sci. Eng. A, 375-377, 213phase stability change of bcc and hcp structures(2004).

in Ti by 6 different alloying elements was[2] Y, Yamabe-Mitarai, K. Yanao, Y. Toda, I.

calculated using DFT. Supercell model with 128

Ohnuma, T. Matsunaga, JALCOM, 911, 164849

atoms was used to indicate solid solution phase.

(2022).

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Analysis of the electronic structure of magnetic shapememory alloy by quasiparticle self-consistent GW approach
Masao OBATA1,2
1

Institute of Science and Engineering, Kanazawa University, Kanazawa, Ishikawa 920-1192
2

Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa,
Ishikawa, 920-1192
Magnetic shape memory (MSM) alloy of Ni-

stoichiometric Ni2MnGa in austenite (cubic) and

Mn-Ga [1] has attracted attention, showing an

non-modulate martensite (tetragonal) phases byenormous magnetic field-induced strain (MFIS)

using the first-principles calculation package

effect of up to around 10%. This unique property

ecalj [3].

comes from transitions among energeticallyAs the result of the QSGW calculation, theclose structures involving nanoscale-modulated

obtained energy level of the Ni eg orbital of thestructures attributed to magnetic and electronic

cubic phase is higher than that of GGA andstats. The MFIS effect is experimentally

almost coincided with the Fermi level. The state

observed in the modulated structure of 10M or

moves from the Fermi level in the tetragonal

14M, which has a long-period crystal structurephase. This result strongly supports martensitic

in the ab plane. Although many investigations oftransformation due to the Jahn-Teller effect.

the electronic structure based on density

Analysis of the Fermi surfaces nesting throughfunctional theory (DFT) within local density

generalized susceptibility revealed that its

approximation

gradient

electronic state of cubic phase possesses

approximation (GGA) were reported, the results

intrinsic instabilities corresponding to 10M anddid not fully incorporate the experimental facts:

14M, which are not observed in the previous

for example, the stabilization mechanism of the

GGA calculation [4].

modulated structures is still unclear. The

References

importance of electron correlation effects in this[1] K. Ullakko et al, Appl. Phys. Lett. 69, 1966

material was recently indicated [2]. Thus, we(1996).

employed one of the post-DFT methods, the[2] M. Zelený et al., Mater. Des., 209, 109917

quasiparticle

orgeneralized

self-consistent

GW(2021).

(QSGW)

approach, to investigate the electronic structure[3] https://github.com/tkotani/ecalj/

beyond the GGA level. This study investigated[4] M. Obata, T. Kotani, T. Oda, Phys. Rev.

the electronic and magnetic structure of theMaterials 7, 024413 (2023)

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Calculation and comparison of adsorption energies ofperfluoro molecules on Cu(111) and Cu(100)using first-principles calculation
Yuji Ohkubo, Misa Nishino, Hiroki Toujima, and Kouji Inagaki
Graduate School of Engineering,Osaka University, 2-1 Yamadaoka Suita, Osaka 565-0871
Recently, increasing frequency of the digital

Our calculations were performed using thesignals is necessary because the amount of

STATE (Simulation tool for atom technology)

information

increasing.

code with plane-wave basis and ultra-soft

However, the higher the frequency is, the greater

pseudopotential[2, 3]. The wave function and thetheTherefore,charge density of cutoff energies were 36 andpolytetrafluoroethylene (PTFE) which has much

400 Ry, respectively. Generalized gradient

low relative dielectric constant and dielectric

exchange-correlation

loss tangent is considered as a suitable dielectricPredew-Burke-Ernzerhof (PBE) was used forof high frequency printed wiring boards.

the exchange-correlation functional and DFT-

However, PTFE has low adhesion property, so

D2 was used for the dispersion correction

adhering PTFE to Cu is difficult. We havemethod[2, 3]. Cu surface was modeled Cu(100)achieved

oxygen-containing

3×3 slab composed of 3 atomic layers. As thefunctional groups and adhesion strength of 0.94

model of as-received PTFE, CF3–CF3 was

N/mm between PTFE and Cu when PTFE wascalculated. In addition, CF3–CF=O, CF3–CF2–

He-plasma-treatedOH,

communicationtransmission

loss

generation

atmospheric

of

at

is.

over

pressure[1].

is

200℃

and

CF3–CF2–OO・

on

were

calculated as the model of plasma-treated PTFE.

calculations of the adsorption energy between

These were the models of molecule containing

Cu(111)theoxygen-containing functional groups observed

functional groups containing COO・ is possible

by C1s-XPS spectrum of the plasma-treatedto generate PTFE surface having high adhesion

PTFE surface. Cu surface, fluorinated molecules,

property. However, the calculations about Cu

and adsorption systems were optimized thensurface having different orientations are nottotal energies were calculated. The adsorption

performed. Therefore, in this study, theenergy was defined as the difference between theadsorption

andsum of total energy of Cu surface and fluorinatedfluorinated molecules was calculated and themolecules and total energy of adsorption system.

fluorinatedenergybetweenfrom

CF2=CF2,

based

the

and

Also,

under

functional

molecules,

Cu(100)Fig. 1 shows the values of adsorption

effect of orientation was investigated.

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

(a)

(b)

Fig. 2 Atomic geometries for CF3–CF2OO・
model (a) on Cu(111) and (b) on Cu(100).
(a)

(b)

Fig. 1 Values of adsorption energies.
energies by calculation. The values of adsorption

Fig. 3 Atomic geometries for (a) CF3–CF2O・model and (b) O model.

energies of CF3–CF3, CF3–CF=O, CF3–CF2–OH,
CF2=CF2, and CF3–CF2–OO・ was 0.25, 0.58,

In this study, the functional groups

0.72, 0.80, and 1.95 eV, respectively. In the casecontributing to improvement in adhesionof molecules containing COO・, the adsorption

property were explored by calculating theenergy was the highest and the molecule wasadsorption

stable as with Cu(100). However, in the case ofgenerating the functional groups containingthat of Cu(111). Fig. 2 shows the optimized

COO・ is possible to generate PTFE surface

atomic geometries for Cu(111) and Cu(100). In

having high adhesion property, regardless ofthe case of Cu(111), CF3–CF2–OO・ wasorientations. Also, in order to further improve

dissociated to CF3–CF2–O・ and O, then they

the adhesion property, it is important toadsorbed. However, in the case of Cu(100),
adsorbed

fluorinatedof Cu(111). In the result of calculations,4.73 eV[2]. The value of Cu(100) was lower thanwasbetweenmolecules and Cu(100) and comparing the result

Cu(111), the value of adsorption energy was

CF3–CF2–OO・

energies

dissociate CO・ and O and adsorb to Cu surface.

without

On the other hands, the calculations of Cu(110)dissociation. This is because the adsorption

and copper oxide is not performed. Therefore,

energy of Cu(100) was lower. The adsorption

these calculations are needed in the future.

energy was calculated when CF3–CF2–OO・ was

Referencesdissociated and adsorbed on Cu(100) as shown[1] M. Nishino et al., INTERFINISH2020 Abstract, C-

in Fig. 3. The adsorption energy was 5.27 eV.

SEP06-004 (2021) 210.

This value was close to Cu(111). These results[2] K. Toyoda et al., J. Chem. Phys. 132 (2010) 134703.

suggest that it is important to dissociate CF3–

[3] K. Toyoda et al., J. Nanosci. Nanotechnol. 11 (2011)

CF2–O・ and O and adsorb to Cu surface, in2836–2843.

order to further improve the adhesion property.

155

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Development of Efficient Evaluation Methods ofBerry-phase-mediated Physical Properties for Large
Scale Systems Using the First-principles LCPAO
MethodNaoya YAMAGUCHI
Nanomaterials Research Institute (NanoMaRi), Kanazawa University
Kakuma-machi, Kanazawa, Ishikawa 920-1192

We did the following researches about thefollowing topics in this subject: (i) development of a linear-combination-of-pseudoatomic-orbital (LCPAO) scheme of a Berryphase method to apply electric fields [1] and(ii) development of a new scheme to theanomalous Hall and Nernst conductivity applicable to metallic systems [2].
For (i), we have developed a first-principles
LCPAO scheme of a homogeneous and finite
electric field method based on the modern theory of polarization and density functional theory (DFT), so that one can consider electricfield effects even for periodic systems such as
bulk insulators. We validated our scheme bycalculating the electronic and static dielectric
constants and Born effective charges of III-V
and II-VI semiconductors and group IV insulating materials. The calculated values coincided with the reported computational values,
and they were an agreement with the experimental values. For the Born effective charges,
we confirmed that the values obtained fromour scheme are consisted with those obtained
from the finite difference method (i.e. numerical differentiation of electric polarization withrespect to atomic positions.) The spin-orbit interaction (SOI) has a non-negligible influenceon the dielectric constants of cases including
the heavy elements. We applied our schemewith the SOI to evaluate the Born effective

charges characterizing the bond properties, covalency and ionicity in new semi-stable phasesof BiTeI including a wide-gap topological insulating phase [3]. Our LCPAO implementation will make it easy to investigate the electronic structures and physical properties under an electric field even for the large system
including hundreds of atoms. In addition, we
also developed the spin texture code for insulators under electric fields through the Berryphase scheme. We did test calculations by applying the spin texture code to bulk diamond.
Bulk diamond is a system keeping the spatial
inversion symmetry, but a strong electric field
can break the symmetry to induce the spin
splitting through the Rashba effect (see Fig.
1 and 2).
For (ii), we also developed the new evaluation scheme for the anomalous Hall andNernst conductivity without constructing theWannier functions or explicit evaluation of the
Kubo formula [2]. We reproduced the intrinsic
contribution of typical anomalous Hall systems
such as body-centered-cubic iron. Our scheme
has advantages of efficiency so that it is the
non-iterative scheme unlike Wannierization.
Therefore, our scheme can easily address not
only large scale cases but also high-throughput
calculations. We applied the scheme to evaluate the peculiar magnetic thermoelectricity inthe ferrimagnetic phases of MnBi2 Te4 layers [4]156Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyoand a ferromagnetic superlattice of tetragonalFe2 Si, that is, Hapkeite [5]. We also developed
another version of the scheme based on the improved tetrahedron method. It is possible todirectly evaluate the pure term of anomalous
Nernst conductivity (ANC) even for systems
with too low the Curie temperature to obtainthe convergent values of the pure term throughthe numerical differentiation of the anomalous
Hall conductivity with respect to the chemical
potential.
We also improved the spin texture code bychanging the eigenvalue solver so that k-spacespin density matrices can be calculated morestably and efficiently. Through the code, weinvestigated a hydroxyl-terminated diamondsurface and found the electronic structure withthe persistent spin helix that is important tospintronic applications [6].
We implemented them to the OpenMX code[7], and they will be released in the future.

Figure 2: Spin texture for the Rashba spin
splitting around the valence band top at the Γpoint induced by an electric field of 3.5 GV/mperpendicular to one of the faces in bulk diamond. The length and direction of arrows
represent the magnitude and direction of the
expectation values of the spin every k-point,respectively.

References[1] N. Yamaguchi, F. Ishii, Comput. Phys.
Commun. 280, 108487 (2022).
[2] H. Sawahata, N. Yamaguchi, S. Minami,
F. Ishii, Phys. Rev. B 107, 024404 (2023).
[3] Y. Zhang, N. Yamaguchi, H. Sawahata, F.
Ishii, Appl. Phys. Express, in press, DOI:
10.35848/1882-0786/acccd3 (2023).
[4] Y. Morishima, N. Yamaguchi, H. Sawahata, F. Ishii, Appl. Phys. Express 16,
043003 (2023).

Figure 1: Band structure with the Rashba
spin splitting around the valence band top at
the Γ-point induced by an electric field of 3.5
GV/m perpendicular to one of the faces inbulk diamond. The momentum shift ∆k was
2.99 × 10−4 Å−1 , and the induced electric polarization was 12.58 µC/cm2 .

[5] T. Tominaga, N. Yamaguchi, H. Sawahata, F. Ishii, Jpn. J. Appl. Phys. 62,
SD1019 (2023).
[6] H. P. Kadarisman, N. Yamaguchi, F. Ishii,
Appl. Phys. Express 16, 023001 (2023).
[7] http://openmx-square.org.

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Electronic structure investigation of the Ba2PrBiO6
photocatalyst
Kazume NISHIDATE
Faculty of Science and Engineering, Iwate University
Ueda 4-3-5, Morioka, Iwate 020-8551

The surface electronic states are calculatedusing a slab model, which consists of stacked
three unit cells and the vacuum region of 16
Å . Where Ba2 PrBiO6 has the Ba-O and the
Pr-Bi polar planes alternately stacked in one
direction. The top and the bottom planes
are terminated with the same polar surface toprevent the artiﬁcial dipole-dipole interactions
over the cyclic boundary. The work function ϕis deﬁned as the diﬀerence between the vacuum
level (ϕvac ) and the Fermi level EF (Figure. 1).
We also performed the Born-Oppenheimer
Molecular Dynamics simulations of the slab toinvestigate its reactiveness to the water. In
this stage, we used the PBE functional for theexchange-correlation and employed only the Γ

Φvac
EF

Energy (eV)A large number of double-perovskite oxides, A2 B ′ III B ′′ V O6 , have been studied dueto their intriguing physical and chemical properties originating in their mixed valence nature. Where the small cations occupy theB’ and B” sites with the diﬀerent valencies.
Recently, it was found that the Ba2 PrBiO6
double-perovskite has a photo catalytic activity in the visible light. However, theoreticalanalysis on the electronic properties of its surface has not been performed. In this work, we
perfomed the ﬁrst-principles density functional
computation of the surface of Ba2 PrBiO6 . We
used the Quantum ESPRESSO (QE) package. We applied the Heyd-Scuseria-Ernzerhof
hybrid functional (HSE06) formalism for theexchange-correlation to evaluate the band gap
as well as the work function.

Z

Z (Å)

Figure 1: Averaged electronic potentials for
the Ba-O polar plane along the Z axis of thesuper-cell.
point for the reciprocal space integration to
suppress the computational time. Temperature was regulated to be 300 K throughout thesimulation time steps using the velocity scaling method. And H2 O molecule was placed at
5 Å over the Ba-O surface. There are threestages in the adsorption process. First, a H2 O
molecule approaches the surface as the resultof the Coulomb interaction between the oxygen and the surface Ba atoms (I). Then, theoxygen atom makes a bond with Ba by releasing its hydrogen atom and thereby forming ahydroxyl group. The released hydrogen atomalso makes a bond with the oxygen atom of
the slab (II). In this adsorption process, twohydroxyl groups were created. We have been
focusing on the detailed analysis on the calculated results.

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Theoretical Study on Electronic Properties
in New Nanoscale Surfaces and Interfaces
Katsuyoshi KOBAYASHI
Department of Physics, Ochanomizu University
2-1-1 Otsuka, Bunkyo-ku, Tokyo 112-8610
sity calculated for the structure with a V-adsorbed
surface is similar to Fig. 2. But, Fig. 2 better reproduces a characteristic feature of the experimental
band dispersion. Therefore it is concluded that theV5 Se8 monolayers in the experiment of Ref. [2] havethe structure shown in Fig. 1.

Energy (eV)We theoretically studied the photoemission spectra of V5 Se8 monolayers. V5 Se8 is a layered material composed of VSe2 layers and V intercalant layers. It is known that bulk V5 Se8 has an antiferromagnetic structure at low temperatures. However,a recent experiment showed ferromagnetic behavior
in V5 Se8 thin ﬁlms [1]. We studied the electronic
structure of V5 Se8 monolayers in 2022 [2].
It may be assumed that V5 Se8 monolayers havea structure composed of two VSe2 layers and a Vintercalant layer as shown in Fig. 1(a). However, a
recent experiment [3] showed a structure that thesurfaces of V5 Se8 monolayers are covered with theV intercalant layers. In order to investigate the surface structures of V5 Se8 monolayers we performed
a density-functional calculation. We used the program package VASP for density-functional calculations.

Wave Number (Έ-1)

Figure 2: Calculated photoemission intensity of theV5 Se8 monolayer. The origin of energy is the Fermi
energy. The wave number is along the direction
perpendicular to the side view plane in Fig. 1.

Figure 1: Side view of the atomic structure of a
V5 Se8 monolayer.

References

Figure 2 shows the photoemission intensity calculated for the structure shown in Fig. 1. The photoemission intensity is a calculated band structure
weighted by the G∥ = 0 component of wave functions, where G∥ are the components of reciprocallattice vectors parallel to the surface. This is dueto that the wave functions in vacuum regions aremainly connected to the G∥ = 0 component in materials. The band structure is calculated in a nonmagnetic state because the photoemission experiment was performed at room temperatures. Theenergy bands are broadened with a width of 0.1 eV.
The calculated intensity has good agreement withthe experimental one [2]. The photoemission inten-[2] K. Sumida et al.: Phys. Rev. B 106 (2022)
195421.

159[1] M. Nakano et al.: Nano Lett. 19 (2019) 8806.

[3] Q. Meng et al.: Phys. Status Solidi RRL 16(2022) 2100601.

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Interaction of magnetic metal phthalocyanines withmetal surfaces
Tetsuya ARUGA
Department of Chemistry, Kyoto University
Kitashirakawa-Oiwakecho, Sakyo, Kyoto 606-8502

Phthalocyanine (C32 H18 N8 ) is a planarmolecule with an extended π electron system.
Two hydrogen atoms in the central “hole” ofthe molecule can easily be substitutid by a 2+transition metal ion, giving rise to metal phthalocyanine (M Pc), and thus the molecule
has two signiﬁcant electronic motifs, conjugated π electrons of a Pc molecule and an openshell d orbital of the central transition metalion.
In this work, we studied the interaction andinterface structure of the metal phthalocyanine
molecules adsorbed on bi-layer indium ﬁlms
fabricated on Si(111)-(1× 1) substrate [1] andstudied the ordering and the interaction of themolecule with the indium substrate. Experimentally, the adsorbed molecules form a twodimensional close-packed lattice. No second
adsorption layer is formed at room temperature, suggesting that the energy gain associated with the planar stacking is weak. On theother hand, the interaction of phthalocyaninewith In substrate appears to be strong, as implied by the formation of close-packed lattice
and the stability against the temperature increase.
In order to clarify the origin of the molecule–surface interaction, we performed a computational work by using VASP [2, 3, 4, 5]. We used
optB86b-vdW van der Waals density functional (vdW-DF) [6] in order to treat the interaction of the extended π-electron system of themolecule with the sp bands of the In substrate.
We used DFT+U method in order to properly

describe the electronic state of the Fe2+ ion in
the molecule. The value of U was set so thatthe magnetic moment of the Fe2+ ion in a free
FePc molecule is reproduced.
The adsorption structure predicted by thecalculation indicates that the adsorption isgoverned by two diﬀerent molecule–surface interactions. The structure obtained by the minimization of the total energy indicated two signiﬁcant characteristics that are not found in
H2 Pc, which is only weakly bound to the surface by vdW-like interaction with the metallic
substrate. Firstly, the Pc molecule is deformed
to a bowl-like structure with the molecular center down and closest to the substrate. Secondly, the Fe ion in the center of the molecule
protrude further downward and appears to be
associatively interacting with the In atoms below. These characteristics are not found for
H2 Pc and CuPc, suggesting that these arecharacteristic to M Pc molecules with open dshell M ions. The interaction between themolecule and the substrate may diﬀer depending on the number of d electrons. The eﬀect ofthese interaction on the superconductivity of
the In ﬁlm[7, 8] is intriguing.

References[1] E. Rotenberg et al.: Phys. Rev. Lett. 91,
246404 (2003).
[2] G. Kresse and J. Hafner, Phys. Rev. B 47,
558 (1993); ibid. 49, 14251 (1994).

160

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo[3] G. Kresse and J. Furthmüller, Comput.
Mat. Sci. 6, 15 (1996).
[4] G. Kresse and J. Furthmüller, Phys. Rev.
B 54, 11169 (1996).
[5] G. Kresse and D. Joubert, Phys. Rev. 59,
1758 (1999).
[6] J. Klimeš et al., Phys. Rev. B 83, 195131(2011).
[7] T. Zhang et al., Nat. Phys. 6, 104 (2010).
[8] T. Uchihashi et al., Phys. Rev. Lett. 107,207001 (2011).

161

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

In-plane interaction of π-conjugated molecules
Tetsuya ARUGA
Department of Chemistry, Kyoto University
Kitashirakawa-Oiwakecho, Sakyo, Kyoto 606-8502

Phthalocyanine (C32 H18 N8 ) is a planarmolecule with a 2D extended π-conjugatedelectron system. Two hydrogen atoms in thecentral “hole” of the molecule can easily besubstituted by a 2+ transition metal ion, giving rise to metal phthalocyanine (M Pc).
The adsorption of M Pc on single-crystalline
metal surfaces has been studied by several
groups. In these works, irrespective of thesubstrate, M Pc molecules form ordered twodimensional lattice, which has common characteristics as below: (1) the molecule adsorbs
with its molecular plane parallel to the surface, (2) the molecules form nearly square lattice that is commensurate with the substratelattice, (3) the molecules are rotated in planeby ∼ 20◦ from the symmetrical direction of thesubstrates and forms a staggered structure.
The ﬁrst point is understood by the fact thatthe adsorption is mostly governed by the vander Waals interaction between the surface andthe molecule. The second point is due to theshape of the molecule, which prefers squarelike lattice to maximize the coverage. The
third point is not obvious. Previous authors
claimed that this is due to the direct chemicalinteraction of N atoms in the molecule with aparticular atoms of the surface, which however
has not been conﬁrmed by quantitative methods.
In this work, we studied (1) the interaction among metal phthalocyanine molecules
adsorbed on bi-layer indium ﬁlms fabricated on
Si(111)-(1× 1) substrate [1] and (2) the interaction among metal phthalocyanine moleculesin the same geometry as in (1) but without thesubstrate.

The total energy was computed by using
VASP code [2, 3, 4, 5] as a function of azimuthal rotation angle. The total energy forthe case (1) shows decrease with increasing azimuthal angle from 0◦ and get minimum at∼20◦ . Further increase of the azimuthal angle leads to the divergence of the total energy due to the direct contact of the neighboring molecules. The energy minimum at∼ 20◦ is in good agreement with the observation. The case (2) gave rise to an almost thesame angular dependence but with the amplitude 60% ∼ 70% of the case (1).

The results indicates that the rotation of
the molecule is induced by the intermolecular interaction, which most probably is dipoledipole interaction between C-H groups of themolecule. The energy minimum is achieved
when the C-H bonds with opposite directionof neighboring molecules approach with eachother most closely. Our calculation does notindicate any signiﬁcant contribution of the direct interaction of molecular N atom with thesubstrate. The reduced interaction in the case(2) suggests that the image dipoles inducedin the substrate (bilayer In) are smaller instrength, which is most probably due to theultra-thinness of the substrate In bilayer, inwhich the metallicity of the lower In layer ismostly lost due to the bonding with substrate
Si atoms.

162

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

References[1] E. Rotenberg et al.: Phys. Rev. Lett. 91,
246404 (2003).
[2] G. Kresse and J. Hafner, Phys. Rev. B 47,
558 (1993); ibid. 49, 14251 (1994).
[3] G. Kresse and J. Furthmüller, Comput.
Mat. Sci. 6, 15 (1996).
[4] G. Kresse and J. Furthmüller, Phys. Rev.
B 54, 11169 (1996).
[5] G. Kresse and D. Joubert, Phys. Rev. 59,
1758 (1999).

163

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Atomic structures and electronic properties ofnanocarbon-based materials
Yoshitaka FUJIMOTO
School of Engineering, Kyushu University
Moto-oka, Fukuoka-city, Fukuoka 819-0395

Graphene is one of the most promissing
materials for electronics devices such as fieldeﬀect transistors and molecular sensors. It
has been reported that the boron atom ingraphene could act as a reactive site for theadsorptions of environmetally polluting NO
and NO2 molecules. [1] Furthermore, the B
atoms in bilayer graphenes are also reported
to enhance the adsorption properties of several
molecules, leading to the possibility to fabricate the highly sensitive NOx sensors. [2, 3, 4]However, it might be diﬃcult to detect harmful
CO molecules using B-doped graphene. Carbon nanotube (CNT) is also expected to be
one of the most promising materials for development of sensors due to its high surfaceto-volume ratio of one-dimensional nanostructures and its excellent electrical conductivity.
Here, we report the adsorption properties ofenvironmetally polluting NOx and harmful CO
molecules on CNTs. [5]We examine the atomic structures of Bdoped and N-doped (10,0) CNTs without theadsorbed molecules. The zigzag nanotubes
such as the (10,0) CNT have two kinds of CC bonds around each atom. In the B-doped
case, two kinds of the B-C bond lengths arefound to be 1.47 Å and 1.50 Å. In the case of
the N-doped case, the C-N bond lengths are
1.37 Å and 1.40 Å. The B-C bond lengths aremuch longer compared with two kinds of the
C-C bond lengths (1.40 Å and 1.41 Å) in apristine (10,0) CNT, while the two kinds of NC bond lengths are slightly shorter than thecorresponding C-C bond lengths. In addition,
the B-C and N-C bond lengths are longer andshorter than those of B-doped graphene (1.47
Å) and N-doped graphene (1.40 Å), respec-164

tively.
We also examine the adsorption propertiesof various molecules including environmentally
toxic molecules on the B-doped and N-doped(10,0) CNTs. Table I lists the adsorption energy and the distance between the moleculeand the B (C) atom in the B (N)-doped (10,0)
CNTs. For the B-doped cases, CO, NO, NO2
and O2 molecules are found to be strongly
adsorbed with relatively large adsorption energies as well as the short binding distances,while CO2 and N2 molecules are weakly adsorbed. For the N-doped CNT, it is reported
that the adsorption energy of a H atom ata neighboring C atom is larger than that at
the dopant N atom itself. Here, we examine
the adsorption of all six types of molecules
at the C atom sites next to N-dopant atoms
in the N-doped CNTs. It is found that only
NO and NO2 molecules are strongly adsorbed,
while the adsorption energies of the remaining four molecules (CO, CO2 , O2 , and N2 )are relatively small and at the same time
the binding distances between molecule and C
atom are relatively long. It is interesting thatthe adsorption energies of these molecules on
the B-doped and N-doped CNTs are overalllarger than those on the B-doped and N-doped
graphenes, respectively, due to the curvatureeﬀects of the nanotubes.
In summary, we have investigated theadsorption eﬀects of environmentally toxic
molecules on atomic structures and energeticsof B-doped and N-doped (10,0) CNTs based on
the first-principles electronic structure study.
The B-doped CNTs can strongly bind with
CO, NO, NO2 and O2 molecules, while CO2
and N2 molecules are not strongly but weakly

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Table 1: Adsorption energy Ea (eV) and distance d (Å) from the B (C) atom for each molecule
adsorbed on B (N)-doped (10,0) CNTs.
NO
NO2
CO
CO2
O2
N2
B-doped Ea −1.32 −1.82 −0.46 −0.06 −0.36 −0.23
d
1.75
1.56
1.54
2.77
1.59
2.71
N-doped Ea −0.97 −0.91 −0.08 −0.07 −0.13 −0.23
d
1.66
1.53
2.74
2.75
1.70
2.84

adsorbed. The N-doped CNTs can strongly
bind with only NO and NO2 molecules in air.
The environmentally harmful NO and NO2
and toxic CO molecules are individually detectable by using B-doped and/or N-doped
CNTs.

References[1] Y. Fujimoto and S. Saito, Jpn. J. Appl.
Phys. 58, 015005 (2019).
[2] Y. Fujimoto and S. Saito, Chem. Phys.
478, 55 (2016).
[3] Y. Fujimoto and S. Saito, Appl. Surf. Sci.
Adv. 1, 100028 (2020).
[4] Y. Fujimoto, Mod. Phys. Lett. B 35,
2130001 (2021).
[5] Y. Fujimoto and S. Saito, J. Electrochem.
Soc. 169, 037512 (2022).

165

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Effects of Element Substitution on Molecular Packing andBand Structures in Single-component Molecular Conductors
with Canted Antiferromagnetism
So YOKOMORI
Department of Chemistry, College of Science
Rikkyo University, 3-34-1 Nishi-Ikebukuro, Toshima, Tokyo, 171-8501
We have investigated the syntheses andelectronic

properties

molecular

conductorsofenergy-correction, by exchanging the original

single-component
exhibiting

elements in the crystal structure of thecanteddiethoxybenzene-fusedgolddithiolene

antiferromagnetism (CAF). The occurrence ofcomplex with other elements. As a result, theCAF in molecular conductors is exceedingly

following findings were unveiled: substituting

uncommonthe

Se led to a reversal of the magnitude of J1 andcorresponding structure-property relationships

J2, as well as a decrease in the bandwidth. This

have yet to be fully disclosed. Recently, asuggests

single-component molecular conductor withsignificantly affect the CAF behavior. In the S-

CAF was produced through the use of a

substituted complex, an interaction appeared

diethoxybenzene-fused

dithiolene

along the inter-conducting layer direction andcomplex (see Fig. 1). In this investigation, wethe bandwidth increased. This outcome implies

centered our attention on the high modulatorythat the dimensionality of spin-spin interaction

capability of substituting elements in theis stronger than that of the original complex,

complex. To enable an effective evaluation ofand thus, the transition temperature of CAF

the systematic structure-property relationship inordering transition could be expected. For the

CAF molecular conductors, we computed the

N-substituted complex, no significant changes

band

element-substituted

were observed except for a slight decrease incomplexes to aid in the synthesis of new

the bandwidth. Therefore, it can be concluded

compounds.

that the priority of synthesizing this complex is[1],

structures

and

of

as

a

gold

result,Band calculations for three kinds of element-

that

Se

substitution

would

low. Accordingly, in this investigation, we

substituted complexes (as depicted in Fig. 1)

successfully

were executed using OpenMX 3.9 software.

complexes should be synthesized next.

For these calculations, the crystal structures

Referenceswere optimized by means of the DFT-D3[1] R. Ishikawa et al., J. Phys. Soc. Jpn., 87,

method which involved vdW- and dispersion

064701 (2018).

166

gainedinsights

into

which

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Fig. 1. Structural formula of the complex of CAF molecular conductor and newly designed elementsubstituted complexes, and calculated band structures. An explanation of band structure changes foreach result are shown in the bottom.

167

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First principles study on the band structures of high entropy
compounds
Hidetomo USUI
Department of Physics and Materials Science,
Shimane University, Nishikawatsu-cho, Matsue, Shimane 690-8504
High-entropy alloys are alloys containing

various elements, spectral weight is reduced. It

five or more elements at one site. Recently,

can be understood that substitution by elements

high-entropy compounds have attracted muchin other groups can suppress the quasiparticle

attention not only from a mechanical point oflifetime but can control the band structure

view but also as functional materials such asaround the Fermi level.

thermoelectricmaterials.[1,2]electronic

and
We

have

structureofcompound MTe

superconducting
investigated
a

high

the

entropy(M = Ag, In, Sn, Pb, Bi),

which exhibits robustness of superconductivity
to external pressure[3, 4]. This year, we
calculated the electronic band structure of MTein the rock salt typed crystal structure using the
VASP package within the PBE-exchange
correlation functional with spin-orbit coupling
included. We used a 2×2×2 supercell of MTe(Sn16Pb16Te32,

In11Pb10Bi11Te32,

In8Sn8Pb8Bi8Te32, and Ag8In6Bi6Sn6Pb6Te32).
We calculated the site arrangement of M atoms
using

the

special-quasi

randomFig. 1: Electronic band structure of MTe

References

structure

implemented in Alloy Theoretic Automated[1] B. Jiang et al., Science 371, 830 (2021)

Toolkit. We took a k-mesh spacing of 0.1 Å-1[2] P. Koželj et al., Phys. Rev. Lett., 113,

and a cut-off energy of 330 Ry. The unfolded

107001 (2014).

electronic band structure of MTe is shown in[3] Md R. Kasem et al., Sci. Rep 12, 7789

Fig. 1. It is found that the band structure for(2022)

M=Sn, Pb exhibits sharp spectral lines, but for[4] Y. Mizuguchi et al., Mater. Today Pnys. 32,

M = Ag, In, Sn, Pb, and Bi, due to a mixture of

101019 (2023)

168

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First-principles study of interfacial magnetic
structure in topological magnets
Susumu MINAMI†,‡ , Yuta TOGA† , Katsuhiro TANAKA††
Department of Physics, Univeristy of Tokyo, Hongo, Bunkyo-ku, Tokyo 113-0033‡
Department of Mechanical Engineering and Science,
Kyoto University, Nishikyo-ku, Kyoto 615-8540

Topological magnetic materials such as
Mn3 Sn and Mn3 Ge exhibit unique properties
due to their particular band structures (Berryphase) and have attracted attention as coreelements for antiferromagnetic spintronics devices. Recently, ﬁnite tunneling magnetoresistance (TMR) eﬀects have been observedat room temperature in the magnetic tunneling junctions (MTJs) with Mn3 Sn [1] and
Mn3 Pt [2], increasing the demand for researchon the interface in antiferromagnetic MTJs.
This research project employed the Quantum ESPRESSO, a ﬁrst-principles calculation code, to analyze the Mn3 Ge/MgO MTJ
and its interface, which have the non-collinear
magnetic conﬁguration.
Due to its noncollinear magnetic structure, the computational cost and output data volume signiﬁcantly increased, and the convergence worsened, necessitating the use of the ISSP Supercomputer.
The study presents the magnetic momentsof the Mn layers at the vacuum surface andwithin the MgO multilayer ﬁlm, with energybased optimization of interfacial distances.
Figure 1 shows the layer dependence of themagnetic moments of the Mn atoms. While
the magnetic moments increase at the vacuum
surface due to increased electron localization,
this increase is not observed at the MgO interface for some Mn positions. The diﬀerent
surface states inﬂuence the magnetic momentsof Mn atoms inside the interface.

Figure 1: Magnetic moments of the Mn atoms
near the surface and the interface.
As the performance of MTJs is inﬂuenced by
the magnetic state near the interface, future
research will investigate charge transfer, ﬁlm
thickness dependence, and their relation to thetunnel conductance.

References[1] X. Chen et al., Nature 613, 490 (2023).
[2] P. Qin et al., Nature 613, 485 (2023).

169

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of TokyoRSDFT calculation of atomic displacements detected throughenergy dissipation measurement on noncontact atomic force
microscopy
Toyoko ARAI1, Takahiro Kato1, Jun-Ichi Iwata2, Atsushi Oshiyama3
1

Graduate school of Natural Science and Technology, Kanazawa University, Kanazawa,
Ishikawa, 920-1192
2

Institute of Innovative Research, Tokyo Institute of Technology, Tokyo 152-8550
3

IMaSS, Nagoya University, Nagoya, Aichi 464-8601

We had experimentally found that, whenbonds on the Si adatom and the Si rest atom,

non-contact atomic force microscopy (nc-we partially terminated the Si(111)-(7×7)

AFM) images showed the high resolution forsurface with H, leading to chemical inactivation

the individual adatoms on a Si(111)-(7×7)

of some of the dangling bonds. Some distinct

surface, the simultaneously obtained energy

bright regions in the dissipation image weredissipation signal increased in close proximity

lost, which coincided with the hollow sites next

over the hollow sites surrounded by a Si

to H-terminated rest atoms and/or H-terminatedadatom and a Si rest atom with a dangling bondadatom (Fig. 1). This indicates that the dangling

per each [1]. To clarify the role of the dangling

bonds of the rest atom and adatom around theFig. 1: (a) nc-AFM image of a partly H-terminated Si(111)-(7×7) surface in a constant∆f mode obtained simultaneously with (b) the dissipation image at ∆fset = –30 Hzwith a cantilever of f0 = 199 kHz. The image area was 6.0×3.5 nm2, sheared at 35°
for drift compensation. The white meshes denote the unit cells. (c) Atomistic modelof the H-terminated Si(111)-(7×7). The estimated H-terminated sites are hatched.

170

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

hollow site can change the dissipation. In this[3]. Next, we calculated the structure changes

study, to elucicate the phenomena, the forces

and the forces acting between the tip and the Si

between Si tip and Si(111)-(7×7) surface were

surface during tip approaching and retracting

calculated using real-space density functional

over the hollow site at the separations between

theory (RSDFT) [2]. RSDFT is a first-

the tip apex atom and the Si adatom on theprinciples program developed by Oshiyama andsurface from 5.0 to 2.0 Å, as shown in Fig. 2.

Iwata, which uses a real-space difference

The atom structure and the force-distancemethod and a pseudopotential method.

curves exhibited hysteresis. The greyed area

First, we optimized the structure of asurrounding by the force-distance curve should

Si(111)-(7×7) reconstructed surface consistingcorrespond to the nonconservative dissipationof 298 Si atoms, the back surface of which wasenergy that was experimentally measured.

terminated with 49 H atoms, and the structure

Furthermore, the calculations with Si(111)-of a [001]-oriented Si tip by RSDFT; the tip(7×7) surface with H-terminated rest atoms or

consisted of 30 Si atoms having a Si dimer at

H-terminated adatom did not show suchthe tip apex, the back side of which was

hysteresis during one cycle of tip travel. Thisterminated with 18 H atoms, named “dimer tip”

calculation for the hydrogen termination was
also in good agreement with the experiment.

References[1] T. Arai, R. Inamura, D. Kura, and M.
Tomitori, Phys. Rev. B 97, 115428 (2018).
[2] J.-I. Iwata, D. Takahashi, A. Oshiyama, T.
Boku, K. Shiraishi, S. Okada, and K. Yabana, J.
Comput. Phys. 229, 2339 (2010).
[3] P. Pou, S. A. Ghasemi, P. Jelinek, T.
Fig. 2 Force–distance (separation) curve withthe optimized structures of the Si tip andSi(111)-(7×7) surface, calculated by RSDFT.

171

Lenosky,S.

Nanotechnology

Goedecker

and

20,

264015R.

Perez,(2009).

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

A study of nickelate superconductors in first-principles
Hirofumi SAKAKIBARA
Department of Engineering,
Tottori University, Tottori, Tottori 680-8525
A

recently

discovered

superconductor

theory and first-principles calculation [5]. In the

NdNiO2 [1] attracts broad interests among thetheory, by applying fluctuation exchange

field of low-temperature physics because of its

approximation (FLEX, Ref. 6) on a seven-orbital

cuprate-likemodelelectronic

structure.

Namely,constructedin first-principle

with

NdNiO2 has infinite layered structure to havemaximally localized Wannier orbital [7], it ismonovalent nickel atoms in which the electron

revealed that the first-principles Hubbard U

configuration is 3d9, the same with the copper

obtainedsite of cuprates. This striking similarity

approximation (cRPA, Ref. 8) is much stronger

motivates researchers to compare the nickelates

than that of cuprates. Therefore, the self-energy

and the cuprates for the purpose of unveiling theinduced by spin-fluctuation damps quasi-

high-Tc pairing mechanism [2].

particles weight to degrade Tc. The main reason

in

constrained

randomphase

However, the nickelates also have severalof this larger U in the nickelates is the muchdissimilarity: (1) Tc is lower (up to 20K, wherelarger energetical splitting Δdp between nickelthat of cuprates are up to 135K), (2) no long-

ion and oxygen.

rangemagnetismsuperconducting
temperature

on

the

vicinity

dome

in

the

phase

diagram,ofConsidering the above numerical results, we

carrier-can easily imagine that Tc in nickelates goes up

(3)

no

by applying hydrostatic pressure that may lower

superconductivity in the bulk materials [3]. Even

U and/or U/t values, where t is the hopping

the feature (1) and (3) is still under intense

integrals of the 3dx2-y2 orbitals between nearest

debate, however, some consensus for (2) is being

neighbor sites. In this research, to confirm thisformed. Namely, since the energy bands formed

idea, I have constructed the seven-orbital modelby lanthanoid atoms overlaps with 3d bands ofby obtaining stable crystal structure under

nickelate unlike cuprates, the self-doped holeshydrostaticfrom the lanthanoid site break magnetic order socomputationalthat no magnetic phase is observed [4,5].

package (QE, Ref. 9) and RESPACK [10]pressuredetails,(0GPa--20GPa).
quantumForespresso

A theory is proposed to interpret the feature

officially implemented in the system B are(1) based on the combination of many-body

employed and the bulk crystal structure is172

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

assumed. It is found that the U/t value becomes

it is found that U itself is roughly scaled by thesmaller by hydrostatic pressure, where the value

valence of nickel atom. These results give us aof t is simply scaled by the lattice constant alonghint to optimize U/t value for seeking higher Tc

the a-axis (along the layers). By performing

materials such as cuprates. As a future task, I

FLEX calculation assuming such smaller U/twill challenge to theoretically explain the recent

value, the larger λ, the eigenvalues ofexperimental result of praseodymium nickelate

Eliashberg equation, is obtained. This means

thin firm in which Tc gets significantly higher bythat Tc may get higher if the hydrostatic pressure

applying pressure [11].

is applied on nickelates.

References

Interestingly, the value of U itself remains
almost constant (or, slightly gets larger) when

[1] D. Li et al., Nature (London) 572, 624 (2019).

the applied pressure gets stronger. To give[2] M. R. Norman, Physic 13, 85 (2020).

deeper insight to this result, I have calculated the[3] Q. Li et al., Communications Materials 1,

value of U in cRPA by varying the valence of1(2020).

nickel atoms. Namely, I have performed virtual[4] A. S. Botana and M. R. Norman, Phys. Rev.

crystal approximation (VCA) for two Sr-doped

X 10, 011024 (2020).

systems La1-xSrxNiO2 and Nd1-xSrxNiO2, where[5] H. Sakakibara et al., Phys. Rev. Lett. 125,

the nickel’s valence is simply formulated as 1+x.

077003 (2020).

In this calculation, the value of U gets smaller as[6] N. E. Bickers et al., Phys. Rev. Lett 62, 961

the valence gets smaller. By constructing the so-(1989).

called d-p model considering all of the 3d-[7] N. Marzari and D. Vanderbilt, Phys. Rev. B

orbitals and 3p-orbitals, it is found that Δdp is

56, 12947 (1997).

roughly scaled by the valence of nickel atoms.

[8] F. Aryasetiawan, Phys. Rev. B 70, 195104

To summarized, I have performed mainly(2004).

two types of calculations employing QE+ R[9] P. Giannozzi et al., J. Phys.: Condens. Matter

ESPACK to capture the possible variation of U/t

21, 395502 (2009)

value in the infinite layered nickelates: structural[10] K. Nakamura et al., Comp. Phys. Comun.

optimization

261, 107781 (2021).

underpressureandVCA

calculation nickelates. The value of U/t may[11] N. N. Wang et al., Nat. Commun. 13, 4367

decrease by hydrostatic pressure while U itself(2022).

possibly remains constant. By VCA calculation,

173

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Growth mechanism of MoS2/MoO2 core shell nanowires
studied by DFT calculation
Toshihiro SHIMADADivision of Applied Chemistry, Faculty of Engineering, Hokkaido University,
Kita 13 Nishi 8, Kita-ku, Sapporo, Hokkaido 060-8628
We have experimentally found that MoS2

concluded that the MoO2 nanowires are grown

nanotubes with rectangular cross sections arefrom some other precursors with excess oxygen.

grown in certain conditions by chemical vapor

Table 1. Surface Energies of Various Faces ofdeposition. After the detailed study of the

MoO2
Faces
(011)
(101)
(110)
(-111)(11-1)(010)(-101)

growth conditions using turbulent flow, wefound that MoO2 nanowires are formed first,
and the surface is sulfurized to make
MoS2/MoO2 core shell nanowires as shown inelectron microscopy images in Fig. 1[1].

Surface energy /Jm-2
2.25
1.84
2.36
2.55
2.55
1.30
1.75

Fig. 1 : Transmission electron microscope
images of MoS2/MoO2 core shell nanowires.
(reprinted with permission: American Chemical
Society[1])In order to elucidate the mechanism of thisgrowth feature, we used DFT calculation

Fig. 2 : Optimized structures of MoO2 surfaces.

(VASP). We studied the surface energies of theReferences

precursor MoO2 (Table 1 and Fig. 2).

[1] M. Goto, I. Yamane, S. Arasawa, T. YAnase,We found that the surface energies do not so

S. Yokokura, T. Nagahama, Y.Chueh, Y. Shin, Y.

differ with each other as to explain the

Kim and T. Shimada, ACS Omega 7, 39362-

nanowire growth by surface stability effect. We

39369 (2022).

174

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First-principles calculation of graphitization of diamondsurface and its exfoliation process
Kouji INAGAKI
Graduate School of Engineering, Osaka University
Yamadaoka 2-1, Suita, Osaka 565-087

Diamond is a material with a highest

not significantly changed by the separation of

mechanical strength and has been applied as

the interface. However, it was found that the C-

cutting tools. However, serious wear occurs in

C bonds at the diamond-Fe interface can be

cutting ferrous materials. As the cause,cleaved by Fe with a low activation barrier ofoxidation of the diamond surface, diffusion ofless than 1 eV, as in catalytic reactions.

carbon atoms into iron, graphitization and its

Furthermore, at the interface where the reaction

exfoliation, etc. have been proposed, but theytakes place, the graphitized layer is found to be

are still unclear. It is well known that graphite

exfoliatedis generally formed on diamond surfaces. The

separation. This study will be submitted forwear is most likely caused by the exfoliation of

publication soon.

this graphite layer. Following the study ofReferences

diamond surface oxidation [1], graphitization[1] J. I. Enriquez, F. Muttaqien, M. Michiuchi,

and its exfoliation on diamond surfaces were

K. Inagaki, M. Geshi, I. Hamada, and Y.

investigated. We found that for a simple

Morikawa Carbon 174, 36-51 (2021).

by

the

subsequentinterfacediamond-Iron interface, the diamond surface is

(a)

(b)

Fig. 1: Peeling of graphitized diamond surface in the separation of diamond and iron interface.
Yellow and red spheres represent carbon and Fe atom. (a) Formed interface between stepped
diamond (111) and Fe (110) surfaces. (b) After interface separated.

175

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

AB type 2D materials database construction by DFT
Masahiro FUKUDA
Institute for Solid State Physics,
The University of Tokyo, Kashiwa-no-ha, Kashiwa, Chiba 277-8581
Demand for high-throughput calculations
based on the DFT is increasing rapidly due todevelopments in data science and artificial
intelligence. In our previous research, weconstructed a structure map [1] and a webbased database [2] of AB2 type monolayers byhigh-throughput

DFT

calculations

using

Fig.1: Structures map of AB type monolayers.

OpenMX. These data also provided on the ISSP

References

data repository [3].

[1] M. Fukuda, J. Zhang, Y.-T. Lee, T. Ozaki,

In this project, a program code to perform
high throughput electronic and phonon band

Mater. Adv., 2, 4392 (2021).

and DOS calculations by using OpenMX [4][2] Structure map and database of AB2 type 2D

and Alamode [5] with Spglib [6] werematerials (www.openmx-square.org/2d-ab2/).

developed to construct materials database. In[3] Structure map of AB2 type 2D materials byaddition, the developed job scripts for massivehigh-throughput DFT calculations

parallel bulk jobs using GNU parallel package[4] OpenMX, http://www.openmx-square.org

enable us to perform high throughput DFT[5] T. Tadano, Y. Gohda, and S. Tsuneyuki, J.

calculations on the ISSP supercomputer ohtaka

Phys.: Condens. Matter 26, 225402 (2014).

effectively and comfortably.

[6] Spglib package (github.com/atztogo/spglib/).

These developed program codes are[7] OpenMX database of Atoms using standard

applied to the AB type structure map (Fig.1)

basis sets, OpenMX database of bulks using

construction,

standard basis set

basicmaterials

database

construction [7], material search for quaternary[8] R. Koshoji, M. Fukuda, M. Kawamura, T.

metal hydrides [8], and AFM simulations toOzaki, Phys. Rev. Materials 6, 114802 (2022).

determine atomic arrangement of Si adatom on[9] Y. Adachi, Z. Runnan, W. Xinbo, M.

the Silicene/Ag(111) surface [9]. Au2X type

Fukuda, T. Ozaki, Y. Sugimoto, Appliedmonolayer study is currently being carried out.

Surface Science, 157336 (2023).

176

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Exploring dopants aiming at achieving high bulk insulation in
Pb-based topological insulators
Yuki TOKUMOTO
Institute of Industrial Science,
The University of Tokyo, Komaba, Meguro-ku, Tokyo 153-8505
PbBi2Te4 topological insulator (TI) is

proposed by Perdew, Burke, and Ernzerhof

considered as one of the promising candidates(PBE) as the exchange correlation energy

for future spintronics material with a large spinfunctional. For all calculations, we included

current density. However, experimental studies

spin-orbit coupling (SOC). We obtained bandof its surface transport properties have not yet

gap of 115 meV for PbBi2Te4, which is muchbeen reported, owing to a lack of bulk

lower than experimentally reported value ~230

insulating crystals. So far, we achieved bulk

meV. In order to estimate band gap moreinsulating states in Pb(Bi,Sb)2Te4 topologicalaccurately,

insulators by tuning compositional ratio of Bi(HSE06) and HSE03 hybrid functional wasand Sb [1]. Furthermore, we recently revealed

employed. As a result, band gap was further

that In doping for Pb(Bi,Sb)2Te4 TI is effective

underestimated. We found that the HSE is not

for further lowering carrier concentrations andeffective for accurate estimation of band gap ofbulk resistivities, which is considered to be dienarrow-gap semiconductors.

theHeyd-Scuseria-Ernzerhof

to a localized impurity band that acts as a

In future, we plan to calculate electronic

charge buffer occurring inside the bulk band

structure of In-doped Pb(Bi,Sb)2Te4 using

gap.

GGA-PBE including SOC.

We carried out band structure calculation of
PbBi2Te4 based on density functional theory

References(DFT)[1] Y. Hattori, Y. Tokumoto, and K. Edagawa:using

Viennaab-initio

simulation

Phys. Rev. Mater. 1 (2017) 074201.

package (VASP) version 6.2.1. We adopted
generalized gradient approximation (GGA)177

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Dielectric function calculations in rare-earth-metal monoxides
Takayuki MAKINO
Research Center for Development of Far-Infrared Region,
University of Fukui, Bunkyo, Fukui, 910-8507
Oxygen in metals is an important and rapidly

be able to determine in ab-initio manner.

expanding field of theoretical and experimental

Second, we should also introduce the Hubbard

study. One of studies is characterization of theenergy on the oxygen. We used a HP

electronic structure of metals containing a

executable

includedindissolved oxygen or ordered oxide phases.

ESPRESSO

package[3].

Compared to the oxygen adsorption metals, anintroduction of the antiferromagnetic order

ordered oxygen is more manageable because it

tends to open the gap even with smaller

can be handled within the framework of band

Hubbard energy. To reconcile with thistheory. Owing to the metastable open-shell

experimental result (paramagnetic), we should

divalent ions, the ground state of rare-earth-assume the antiferromagnetic order is effective

metal monoxides are not trivial. In some cases,

only within a short-range. To check this, we

it results in a metallic ground state, while inconfirmed that the antiferromagnetic insulatingother cases, it yields in an insulator. Theseground state is energetically favored.

monoxides can be viewed as prototypes of thethe

Quantum

Finally,the

The Hubbard energy for the rare-earth islarge number of metallic monoxides which

evaluated to be ca. 1 eV. The calculation underform in the same NaCl structure.

this condition yielded in a metallic ground state.

According to pioneering work [1], lineshape

If we wish to open the gap, the Hubbard energy

analysis of the spectra revealed that a certain

must be 9 eV. This fact made the authors

rare-earth monoxide is an indirect-type narrow-reconsider about a ground state of our rare-

gap Mott-Hubbard insulator, having a gap of ca.

earth-metal monoxide system.

0.2 eV [1]. The author has tried to reproduce

We used a 50 Ry energy cutoff for thethis in the DFT- based calculation. To open theelectronic wave functions and a small smearing

gap, there are representative three knocks. First,

width of 0.02 Ry which required a 6×6×6 k-

we should introduce an on-site Coulomb energy,

point mesh. Adopted pseudopotentials arewhich is otherwise known as the Hubbard

based on the projector augmented-wave method.

energy [2]. Recently this has been turned out to178

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

We introduced that (Up) on the p orbital of thesynthesized in a thin film state. Then, we areoxygen atom. The value of Up (ca. 21 eV) has

interested in the Hubbard energy dependence ofbeen determined by the self-consistent linear

the optical conductivity and dielectric function

response

spectra for this compound.

approach.

To

implementtheantiferromagnetic ordering, we adopted four-

We are now calculating various types of theatom-configuration including two cations with

opticalup and down spins.

spectrafor

thisrare-earth-metalmonoxide. We used the Wannier executable

The judgement for narrow-gap insulator has

included in the Quantum ESPRESSO package

been based on the absorption spectrum. The[3]. By increasing the Hubbard energy in the

Drude edges are less noticeable viewed in thisDFT calculation, the infrared divergence in the

spectrum. It is no longer true in cases ofconductivity spectrum is suppressed. On one

photoconductivity

function,

hand, in addition to the optical absorption

dominated by large Drude tails. The authors [1]spectrum, we are now constructing the opticalhave presented the dependence of the electricalconductivity spectrum based on the same set of

conductivity on the partial oxygen pressure

the measured data, i.e., transmissivity andduring the growth. They identified that thereflectivity.

ordielectric

specimen showing the lowest conductivity
should be classified into a narrow-gap insulator
rather than a (correlated) bad metal. We
speculate that even this one should be a gaplesscompound. In the measurement of thin films, it

References

is impossible to accurately and precisely[1] K. Kaminaga et al., ACS Omega 2018, 3,

quantify the small optical absorption coefficient

10, 12501.

region. Accurate information in the region of[2] B. Himmetoglu et al., Phys. Rev. B. 84,

small coefficients is essential for determining

115108 (2011).

whether it is indirect or direct transitions. This

[3] P. Giannozzi et al., J. Phys.: Condens.

condition cannot be satisfied in the case of ametastable

substance

that

can

only

Matter 21, 395502 (2009).

be

179

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Electronic Structure and Fermiology of d- andf-electron compounds
Isaki Kinjo, Juno Iraha, Yasutomi Tatetsu1 , and Takahiro Maehira2
Graduate school of Engineering and Science, Nishihara, Okinawa 903-0213
1
Center for Liberal Arts Education, Meio University, Nago, Okinawa 905-8585
2
Faculty of Science, University of the Ryukyus, Nishihara, Okinawa 903-0213

TaC
100 [%]

2.0
1.9
1.8
1.7
1.6
1.5
1.4
1.3
1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0

EF

W
Z

Γ

L
Q

Λ

Γ

X K
Δ

Ta d component

Energy [Ry]The calculation for an energy band structureof TaC was carried out using a relativistic linear augmented-plane-wave (RLAPW) method.
f electrons are assumed to be itinerant andthe calculation is performed in the paramagnetic phase. Note here that relativity should
be taken into account, because of large atomic
numbers of the constituent atoms. The spatial shape of the one-electron potential is determined in the muﬃn-tin approximation, andwe use the exchange and correlation potential
in the local density approximation (LDA)[1].
The crystal structure of TaC is categorized into
the NaCl-type structure, which is characterized by the space group Fm3m[2, 3, 4, 5, 6, 7].
The iteration process for solving the Dirac oneelectron equation starts with the crystal charge
density that is constructed by superposing therelativistic atomic charge densities for neutral atoms Ta([Xe]4f 14 5d3 6s2 ), and C(2s2 2p2 ),where [Xe] symbolically indicate the closed
electronic configuration for xenon. In Figure 1,
we show the energy band structure for TaC
along the symmetry axes in the Brillouin zone
in the range from 0.0 to 2.0 Ryd. First notethat in the vicinity of the Fermi level EF located at 1.2860 Ryd., there occurs hybridization between Ta 5d and C 2p states. The number of the valence electrons in the APW spherepartitoned into the angular momenta is 0.612(s), 0.993 (p), 3.327 (d) and 0.381 (f ) in the
Ta sphere, 0.570 (s), 1.044 (p), 0.006 (d) and
0.000 (f ) in the C sphere. There are 2.012 va-0Σ

Figure 1: Energy band structure for TaC calculated by using the self-consistent RLAPW
method.EF indicate the position of the Fermi
level.The contribution of Ta-5d electrons to theEnergy band is illustrated by color, where redcorresponds to 100% (Ta-5d).

lence electrons outside the APW sphere in theprimitive cell.
Now we discuss the Fermi surfaces of TaC.
Since the lowest four bands are fully occupied,
as shown in Figure 1, the next three bands
are partially occupied, while higher bands are
empty. Namely, the 5th, 6th, and 7th bands
crossing the Fermi level construct the hole orelectron sheet of the Fermi surface, as shown180

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

(a)

0

(b)

100 [%](c)

100 [%]

00100 [%]

Figure 2: Calculated Fermi surfaces of TaC for (a) 5th band hole sheets, (b) 6th band electron
sheets, and (c) 7th band electron sheets. Colors indicate the amount of 5d angular momentum
character on each sheet of the Fermi surface. Red-shift indicate the increase of the admixtureof d electrons. The center of the Brillouin zone is set at the Γ point.
in Figure 2. Note that the Fermi surfaces from
5th, 6th, and 7th bands are shown in (a), (b),and (c). The Fermi surface from the 5th bandconsists of two equivalent small hole sheets
centered at the K points and one large hole
sheet centered at the Γ point. The 5th band
constructs a large hole sheet centered at the Γpoint, which exhibits a complex network consisting of big “arms” which lie along the edgesof Brillouin zone, as observed in Figure 2(a).
The hole sheet centered at the Γ point is shownin Figure 2(b). It looks like a rounded cube
having eight bumps in the <111> directions.
The 7th band constructs a small electron sheet,
which is centered at the Γ point is shown in
Figure 2(c). The total number of holes is notequal to that of electrons, which represents
that TaC is a uncompensated metal. Let us
turn our attention to the analysis of the angular momentum character of the states forming
various sheets of the Fermi surface. Here themost important quantity is the amount of Ta
5d character, which is the partial density ofstates for Ta 5d state for each point in k-spaceon the Fermi surface. This quantity is visualized in Figure 2, where the admixture of the Ta
5d states is increased as the red shift in color,
as shown in the scale diagram. The broad vari-ation of the color from blue to red reflects a
substantial variation of the 5d contribution fordiﬀerent groups of states which change in therange from about 15% to about 80%. However,this distribution is diﬀerent from part to parton the Fermi surface.

References[1] O. Gunnarsson and B. I. Lundqvist: Phys.
Rev. B 13 (1976) 4274.
[2] Nowotny H., and Kieﬀer R.Z.: Metallkd.
38 (1947) 257.
[3] Allen P.B., and Cohen M.L.: Phys. Rev.
Lett. B29 (1972) 1593.
[4] Klein B.M., and Papaconstantopoulos
D.A.: Phys. Rev. Lett. B32 (1974) 1193.
[5] Mulokozi A.M.: J. Less-Common Met. 79(1981) 139.
[6] Allison C.Y., Finch C.B., Foegelle M.D.,
and Modine F.A.: Solid State Commun.
68 (1988) 387.
[7] Frisk K., and Fernandez Guillermet A: J.
Alloys Compd. 238 (1996) 167.

181

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Electronic Structure of 2D Boron Monosulfide
Masayuki TOYODA
Department of Physics, Tokyo Institute of Technology
Ookayama 2-12-1, Meguro-ku, Tokyo 152-8551

Ultrathin two-dimensional (2D) materials
are a class of nanomaterials that exhibit uniquemechanical, physical and chemical properties.
Among them, 2D materials consisting of boron
has been now attracting increasing interest
due to their polymorphic properties. Recently,
ultrathin boron monosulfied (BS) nanosheets
have been successfully synthesized by the physical exfoliation of rhombohedral boron monosulfide (r-BS) [1]. The discovery of the BS
nanosheets raised attention to their bulk layers. We have investigated the electronic andlattice vibrational properties of r-BS by performing first-principles density-functional theory (DFT) and density-functional perturbation theory calculations. The results agrees
well with the experimental measurements including the infrared absorption spectroscopy[2] and microfocused angle-resolved photoemission spectroscopy [3]. By incorporatingwith the experiment, we have revealed the detailed electronic structure of r-BS and its ptype semiconductivity. In this study, we have
utilized the eﬃcient parallel computing powerof ISSP Supercomputer System B (ohtaka).

Figure 1: Calculated electronic structure of rBS. The band dispersion (a and b) and 2D
cross-section at diﬀerent energies (c, d, and e)
are shown.

References[1] H. Kusaka, R. Ishibiki, M. Toyodaet al.,J. Mater. Chem. A 9, 24631-24640 (2021);
J. Mater. Chem. A 10, 4999 (2022).
[2] 0 N. Watanabe, K. Miyazaki, M. Toyoda
et al., Molecules 28, 1896 (2023).
[3] K. Sugawara, H. Kusaka, T. Kawakami etal., Nano Lett. 23, 1673-1679 (2023).

Figure 2: Calculated phonon dispersion of rBS.

182

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Exploration of exotic surface sites for catalyst informatics
Yoyo HINUMA
National Institute of Advanced Industrial Science and Technology (AIST)
1-8-31, Midorigaoka, Ikeda, Osaka 563-8577
Surface point defects of metal oxides, forshown in the setting of a 2x2x2 supercell of theinstance O vacancies, have a dominant effect onheterogeneous

catalysis.

The

cubic 5-atom.

Mars-Van

Krevelen mechanism is one of the mostfrequently encountered catalytic process. In one
example, O vacancies on a metal oxide catalyst
surface act as reaction sites. The energy
minimum required to remove O from a surface,which is denoted as the surface O vacancy
formation energy (EOvac), can be used torationalize and predict catalytic performance in

Fig. 1. Crystallographic conventional cell ofsuch a catalytic process. Calculation of EOvac

CaTiO3 (space group type Pnma, number 62).

requireswith

Orange, blue, and red balls represent Ca, Ti, andsufficient spacing between O vacancies, henceO, respectively. The directions of basis vectors

some estimation of EOvac from less costly

are shown in the orthorhombic (ort) and cubic

calculations, such as slab-and-vacuum model(cubic) settings/aslab-and-vacuum

model

calculations with minimum cell size and evenbulk calculations, will be effective in screening

The following trends were found:-Metallic compounds had small EOvac; less thanmaterials for a given purpose.
Computational exploration of previously

2 eV and 1 eV for A-cation and B-cation

unknown reactive sites is a powerful strategy for

terminated surfaces, respectively.

emergence of new catalytic reactions. The-Ti-containing compounds had very high EOvac

surface energies of experimentally knownwith more than 4 eV for the B(Ti)-cation layer.

perovskite oxides with B-site cations with redox

Thiscapability (Ti-Fe). The A-cation and B-cation

perovskites are not very reactive.

terminated (100) surfaces were studied. The-Other compounds were in-between, with thesymmetry was reduced by hand to obtain thetrend of higher A-cation layer EOvac when the B-

lowest energy structure. Fig. 1 shows CaTiO3

cation contains more 3d electrons

183

resultimpliesthat

Ti-containing

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Atom correlation of two-dimensional Cu-Ni alloy on theNi(110) and Cu(110) surfaces
Tsuneo FUKUDA
Division of Electronics and Physics, Graduate School of Engineering,Osaka Metropolitan University, 3-3-138 Sugimoto, Sumiyoshi-Ku, Osaka 558-8585
Copper-Nickel

alloysareubiquitousin the inset of Fig. 1 and Table 1.

materials, and they are widely used as
construction materials such as piping andvarious coinages. Cu and Ni are both simple fcc
metals and form a uniform solid solution
without any intermetallic compounds. The
Fig. 1: Interaction energy as function of atomic distance.
Red circles are for Cu-Cu pairs on the Ni(110) surface,and blue squares are for Ni-Ni pairs on the Cu(110)
surface. Unit cell is indicated by a red broken rectangle
in inset, and embedded Cu atoms for n=2 are indicatedby green.

interaction energy is, however, known to bepositive, resulting in a spinodal decomposition
which has been established theoretically andexperimentally. However, due to the lack of

Table 1 Parameters for the present calculation

definitive experimental studies, no quantitative

VASP Version
slab geometry
k-points
potential
cut off energy

evaluation of the interaction energy between Cu
and Ni has ever been conducted.
Here we studied atom correlation of two-Fermi level smearing

dimensional alloy on Ni(110) and Cu(110)convergence condition

surfaces. The (110) face of fcc metals have a

SCF mixing
parameters

unique one-dimensional atom chain, and we

6.3.2
1×12 units 9 layers
32×4
PBE [2]384.08 eV
Methfessel & Paxton
 = 0.12(Ni), 0.3 (Cu) eVElectronic:10-6 eVIonic: 0.01 eV/Ang.
AMIX = 0.02,BMIX = 0.0001
AMIX_MAG = 0.08
BMIX_MAG = 0.0001

one-

The results shown in Fig. 1 clearly indicate

dimensional attractive interaction of Cu-Cu on

attractive interaction both Ni-Ni and Cu-Cu

the Ni(110) surface.[1] The aim of this study ispairs, which is direct confirmation of theto evaluate the interaction energy from the first-

clustering behavior of the two-dimensional Cu-

principles. We used VASP, and by changing theNi alloy.

Cu-Cu (Ni-Ni) distance on the Ni(110)

References(Cu(110)) surfaces the interaction energies[1] T. Fukuda, T. Saito, I. Kishida, ACSIN-14,

were extracted as a function of Cu-Cu (Ni-Ni)

Sendai 2019, 23E28.

distance (n). Atomic structures and the[2] J. P. Perdew, K. Burke, and M. Ernzerhof,

calculation parameters are respectively shown

Phys. Rev. Lett. 77 (1996) 3865.

haveexperimentallydemonstrated

184

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Search and realization of novel electronic properties of
surfaces and interfaces and of nanostructures
Takeshi INAOKA

Department of Physics and Earth Sciences, Faculty of Science,
University of the Ryukyus, 1 Senbaru, Nishihara, Okinawa 903-0213
This academic year, using the van-derWaals density-functional theory, weinvestigated the electronic structure of
the x-form phthalocyanine (Pc) crystal
and the iodine doping effect on thisstructure. The Pc is a planar conjugated macrocyclic molecule which
can include an atom of various species at
its center. The x-form is one of several
typical stacking forms of Pc in whichmolecular planes are perpendicular to thestacking axis. The x-form crystal iscomposed of a simple tetragonal lattice.
In each primitive unit cell of x-LiPc or
x-NiPc, there are two molecules which
are stacked along the c axis with astaggered angle of about 40°. The x-LiPc
or x-NiPc is considered to be a Mott
insulator because of its half-filled band.
On iodine (I) doping, I atoms penetrate
into one-dimensional (1D) open channels
between x-form molecular chains, which
results in x-LiPcI [1] or x-NiPcI [2].
Although x-LiPc or x-NiPc is a Mott
insulator because of its half-filled band,
doped I atoms oxidize partially Pc chains,
shift the band occupancy from half-filling,
and consequently leads to metallic nature[1]. In this analysis, we chosen SiPc and
SiPcI, because a monomer of SiPc has a
Si pz-derived orbital just above the LUMO,
and we expected x-SiPc to be 1D Si metal.
However, as described below, dimerizationof Si turns out to prevent formation of ametallic band.
We employed the program package‘Vienna Ab initio Simulation Package’(VASP) [3,4] on system B to optimize ourstructures.
To visualize our system, we show the
optimized x-SiPcI structure in Fig. 1.
First, we consider the case without I
doping. If we assume one period in thestacking direction, namely, one molecule

185

in each primitive unit cell, we obtain ametallic band with about one-thirdoccupied, and the cohesive energy permolecule of 1.82 eV. However, this is not
the ground state.
In a single SiPc chain with two period,
two Pc molecules in each primitive unit
cell are stacked with a staggered angle
45°where eight C atoms nearest to thecenter Si atom in one molecule accord
with those in the other molecule inprojected position. This angle relation
enables overlapping of pz orbitals ofadjacent molecules at these C-atom
positions. The cohesive energy is 3.41 eV
which is larger than that of the above
one-period system.
In the x-SiPc crystal without I doping,which includes intermolecular interaction,
the staggered angle deviates to 38.4°.
Although this angle is somewhat
unfavorable in terms of the abovementioned overlap of C-atom pz orbitals,
s-orbital overlap occurs between two H
atoms at the outer edges of neighboring
molecule planes, as shown in the total
electron density distribution in Fig. 2. Thisoverlap can be ascribed to the fact that a
number of k-states in some -bonddominant bands about 7 eV below the
(a)

(b)

Fig. 1 Optimized x-SiPcI crystal structure. (a)top view, (b) side view. As for circles, blue: Si,
gray: N, brown: C, white: H, purple: I.

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

x-SiPcx-SiPcI

(a)

(b)

2

2

E-EF (eV)b199

Fig. 2 Total electron-density distribution of
x-SiPc. The purple circle shows s-orbital
overlap between two H atoms.

1b197,198

0-1

Fermi level have local orbitals like H
molecules. The Si atoms dimerize in thestacking direction, as in Fig. 1 (b), which
prevents formation of a metallic band
owing to Si pz orbitals. The moleculecohesive energy is as large as 4.01 eV.
Figure 3 (a) exhibits the band dispersion
along the -Z line in the stacking direction.
The band b185, which is fully occupied,derives from Si pz orbitals. Two almost
degenerate bands, each of which isspin-degenerate, are half-filled ones,
which leads to Mott insulators, according
to the experimental results of x-LiPc [1]or x-NiPc [2].
Iodine (I) doping to x-SiPc turns thisinsulator into a metal. Figure 3 (b) displays
the band dispersion along the -Z line for
x-SiPcI. We can identify the I-derived
bands by following the energy levels
labeled red in this panel. The highest oneof these band dispersions that passes thelevels b199 and b195 decreases rapidly inenergy and intersects the Fermi level.
Since this implies appearance of a metallic
band, we analyzed the electronic
structure. Figures 4 (a) and (b) shows the
top and side views of the electron density
distribution of the state at the Fermi level
which is arrowed and marked b195 in red
in Fig. 3 (b). The metallic states along the I
atomic chains are connected to electron systems extending around theouter edges of Pc molecular planes. We
consider that I doping makes a wide rangeof x-form Pc crystals metallic, including
LiPcI and NiPcI.
To verify existence of 1D metallic bands
due to I atomic chains, we are performing
further calculations taking more k points in

186

1

b190,191
b188,1890

b195,196
b195

b187

b194

b186

-1 b192,193
b188, b191
189

b185

-2

-2



Z



Z

Fig. 3 Band dispersion of (a) x-SiPc and (b)x-SiPcI along the c axis. The bands labeled in
red in (b) stem from the I-atomic chains, andone of these bands crosses the Fermi level, as
is eye-guided by a red broken line.

the Brillouin zone and upgrading the
calculational scheme to the hybrid density
functional method with higher accuracy.
We will report the confirmed results in anear future [5].
(a)

(b)

Fig. 4 Electron-density distribution of the stateat the Fermi level that is arrowed and labeled
b195 in red in Fig. 3 (b). (a) top view, (b) side view.

References[1] R. Teruya et al., Angew. Chem. Int. Ed. 61,
e202206428 (1-6) (2022).
[2] C. J. Schramm et al., J. Am. Chem. Soc. 102,6702-6713 (1980).
[3] G. Kresse and J. Hafner: Phys. Rev. B 47,
558-561 (1993).
[4] G. Kresse and J. Furthmüller: Comput. Mat.
Sci. 6, 15-50 (1996).
[5] T. Inaoka, to be submitted.

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

DFT calculations of organic halogen molecules and their reaction products
on metal surfaces
Noriyuki TSUKAHARANational Institute of Technology, Gunma College, Toriba-machi 580, Maebashi, Gunma,371-0845, Japan.

In this study, DFT calculations were carriedout to clarify the contribution of intermolecular
halogen and hydrogen bonds to the structuresof the 1,3,5-tris(bromophenyl)benzene (TBB)
molecular film on Ag(111) and Si(111)(! 3"! 3)-Ag surfaces [1]. In combination with
STM experiments, the structure of TBB
molecular

films

and

the

intermolecularinteractions are clarified. Fig. 1 shows thespatial distribution of the differential charge
density for the isolated TBB molecule and TBB
assembly on Ag(111). The difference in theFig. 1: The spatial distribution of the differential charge

distribution between the isolated molecule anddensity for the isolated TBB molecule and the TBB

the TBB assembly is derived from electrostatic

assembly on Ag(111), and the vertical cross section of

attraction with a Br atom in the neighboring

the distribution along the line in (a) and (c). Reprinted

TBB molecule for the TBB assembly. It

with permission from N. Tsukahara and J. Yoshinobu,

indicates intermolecular halogen bond. Similar

Langmuir, 38, 8881 (2022). Copyright 2022, American

calculation

Chemical Society.

indicates

theintermolecularhydrogen bond on Si(111) (! 3" ! 3)-Ag.

References

In addition, STM and DFT calculations
were carried out to clarify the growth[1] N. Tsukahara and J. Yoshinobu, Langmuir,

mechanism of the metal cluster using the metal-

38, 8881 (2022).

organic layer obtained by coupling reaction of[2] N. Tsukahara, R. Arafune, and J. Yoshinobu,

TBB molecules on Ag(111) as a template [2].

Langmuir,

187

to

be

submitted.

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

3.3

Strongly Correlated
Quantum Systems

188

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Numerical Studies on
Superconductivity of Bilaye Cuprates andMott Insulating States of Kitaev Materials
Youhei YAMAJI
Research Center for Materials Nanoarchitectonics, National Institute for Materials Science
Namiki, Tsukuba-shi, Ibaraki 305-0044

We have studied properties of two families of typical correlated electron systems, cuprate high-temperature superconductors and quantum-spin-liquid candidates, byusing many-variable variational Monte Carlo
methods [1]. As detailed below, we foundthat adjacent CuO2 layer does not improvestability of the d-wave superconductivity in
Hubbard-type hamiltonians [2], which are effective hamiltonians for the bilayer cuprates.
We also studied propertie of an ab initio hamiltonian of a Kitaev material, Na2 IrO3 [3, 4].
We studied the superconducting correlation
function at long distance, Pd , for the bilayer
t-t0 Hubbard model around U/t = 10 andt0 /t ⇠ 0.3. When we change a hole concentration, we found that the maximum Pd isquantitatively similar for the single-layer andbilayer t-t0 Hubbard models [2]. From the momentum distribution functions, nk , we estimated the size of the superconducting gap .
Then, the e↵ective attractive interaction, Vd ,pcan be inferred by Vd = / Pd . For both of
the single-layer and bilayer systems, we obtain
Vd ⇠ 1.7t. The variety of the optimal criticaltemperatures of cuprates may originate fromthe screening from apical oxygen and, thus,
mainly from the Hubbard U [5, 6].
We also studied the properties of Na2 IrO3
beyond low-energy spin degrees of freedom.
First, we clarified the ground state of the abinitio t2g hamiltonian [3]. We confirmed thatthe ground state shows the zigzag magnetic or-der, and found that the the amplitude of theMott gap is 750 meV, which is larger than 340
meV reported by combining the optical conductivity and photoemission spectroscopy [7].
The discrepancy originates from the MottAndern nature of the experimentally observed
samples of Na2 IrO3 due to disorders, where theMott gap is filled by in-gap localized states.
When the principle energy scales, the on-site(o↵-site) Coulomb repulsion U (Vij ) and spinorbit coupling ⇣so , are deviated from the abinitio values, the first order phase transitions
from the zigzag state to ferromagnetic andcharge ordered states are found.

References[1] T. Misawa, et al.: Comput. Phys. Commun. 235, 447 (2019).
[2] A. Iwano and Y. Yamaji: J. Phys. Soc.
Jpn. 91, 094702 (2022).
[3] Y. Yamaji, et al.: Phys. Rev. Lett. 113,107201 (2014).
[4] C. Xu and Y. Yamaji: in preparation.
[5] J.-B. Morée, et al.: Phys. Rev. B 106,
235150 (2022).
[6] M. T. Schmid, et al.: arXiv:2303.06672.
[7] R. Comin, et al.: Phys. Rev. Lett. 109,
266406 (2012).

189

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Theoretical study of strongly-correlated topologicalphenomena by exploiting first-principles calculations andmachine learning
Yukitoshi MOTOME
Department of Applied Physics,
The University of Tokyo, Bunkyo, Tokyo 113-8656
We have theoretically studied a variety of

the number of Co kagome layers and the

intriguing topological phenomena in strongly-

surface termination, which will be useful forcorrelated electron systems by using numericalfuture experimental fabrication [4].

methods including first-principles calculations(iii) Topological spin crystals as spin moire:and machine learning (project numbers: 2022-

Extending the research in last year, we clarified

Ca-0013 and 2022-Cb-0013). During the last

the effect of phase shift on skyrmion andfiscal year, we have achieved substantial

hedgehog

progress

topological phase diagrams [5]. We also

on

the

following

topics.

We

crystalsandelucidated

thesummarize the main results below.

showed that hedgehog crystals can be stabilized(i) Development of new theoretical methods:

in centrosymmetric cubic lattices by itinerant

We developed a new framework to solve

frustration mechanism [6]. In addition, we“inverse problem”, namely to construct adeveloped a new exact method to solve

proper Hamiltonian which exhibits desired

thermodynamics of effective infinite-range

physicalinteraction models [7], and by applying it weproperties,

by

using

automatic

differentiation [1]. We also proposed a new

unveiled

scheme for physical reservoir computing by

associated with pair creation and annihilation of

exploiting frustrated magnets, which is usefulemergent magnetic monopoles [8].

to realize efficient parallel computing in both(iv) Kitaev quantum spin liquids: By using thespace and time [2,3].

pseudo-fermion(ii) Topological properties of thin films of Weylgroup (PFFRG) method, we clarified thesemimetals: Studying the structural, electronic,

feasibility of Kitaev quantum spin liquids inand magnetic properties of atomically thinultracold polar molecules [9], a complete phasefilms of Co-based shandite by first-principles

diagram for the Kitaev-Heisenberg model withcalculations, we clarified the changes ofarbitrary spin length S [10], and the phasemagnetism and band topology with respect todiagram for the S=1/2 Kitaev-Heisenberg

190

hiddentopological

functionaltransitions

renormalization

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

model on a three-dimensional hyperhoneycomb

preprint (arXiv:2212.09026).

lattice [11]. We also unveiled several exotic[5] K. Shimizu, S. Okumura, Y. Kato, and Y.

phases, including multiple-q chiral phases andMotome, Phys. Rev. B 105, 224405 (2022).

chiral spin liquid, in competing region between[6] S. Okumura, S. Hayami, Y. Kato, and Y.

spin nematics and spin liuqids in the S=1

Motome, J. Phys. Soc. Jpn. 91, 093702 (2022).

Kitaev[7] Y. Kato and Y. Motome, Phys. Rev. B 105,

model

with

bilinear-biquadraticinteractions by using the SU(3) formalism [12].

174413 (2022).

We elucidated linear and nonlinear thermal[8] Y. Kato and Y. Motome, Phys. Rev. B 107,transport in the presence of both uniform and094437 (2023).

staggered magnetic fields [13]. In addition, we[9] K. Fukui, Y. Kato, J. Nasu, and Y. Motome,

clarified that the dynamical spin transport

Phys. Rev. B 106, 014419 (2022).

induced by an AC magnetic field applied to an[10] K. Fukui, Y. Kato, J. Nasu, and Y. Motome,edge of the system is useful for the

Phys. Rev. B 106, 174416 (2022).

identification of fractional excitations in the[11] K. Fukui, Y. Kato, and Y. Motome,

Kitaev quantum spin liquids [14]. Furthermore,

preprint (arXiv:2303.09156).

we found a phase transition in the quantum spin[12] R. Pohle, N. Shannon, and Y. Motome,

liquid phase under a magnetic field in α-RuCl3

Phys. Rev. B 107, L140403 (2023).

in collaboration with experimentalists [15]. We[13] K. Nakazawa, Y. Kato, and Y. Motome,

also wrote a review article (in Japanese) [16] inPhys. Rev. B 105, 165152 (2022).

a special issue for “New Developments on the[14] T. Misawa, J. Nasu, and Y. Motome,

Physics of Kitaev Spin Liquids” edited by the

preprint (arXiv:2304.00528).

author and coworkers [17].

[15] S. Suetsugu, Y. Ukai, M. Shimomura, M.
Kamimura, T. Asaba, Y. Kasahara, N. Kurita, H.

References

Tanaka, T. Shibauchi, J. Nasu, Y. Motome, and[1] K. Inui and Y. Motome, Commun. Phys. 6,

Y. Matsuda, J. Phys. Soc. Jpn. 91, 124703

37 (2023).

(2022).

[2] K. Kobayashi and Y. Motome, preprint[16] 張 成燻、求 幸年：固体物理 57, 757(arXiv:2302.14496).

(2022).

[3] 特願 2023-025883「情報処理システム、[17] 固体物理特集号 57, 613-784 (2022), "キ

情報処理方法およびプログラム」求 幸年、

タエフスピン液体の新展開" (編集委員：芝小林海翔（2023 年 2 月 22 日出願）

内孝禎・遠山貴巳・求 幸年)[4] K. Nakazawa, Y. Kato, and Y. Motome,

191

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Theoretical study on elementaly excitations andoptical control of topological phases in the
spin-charge coupled magnets
Rintaro ETO and Masahito MOCHIZUKI
Department of Applied Physics, Waseda University
Okubo, Shinjuku-ku, Tokyo 169-8555

We have theoretically studied elementaly
excitations and photoinduced topologicalphases in the Kondo-lattice magnets withcouplings between spin and charge degrees
of freedom (project ID: 2022-Ca-0012, 2022Cb-0014). Results of these projects are listed
below.

(a)

(b)

(c)

Figure 1: (a) Schematic of the quadruple-Q
hedgehog lattice. Magenta and cyan balls represent hedgehogs and antihedgehogs, respectively. Red and blue dashed lines represent
the Dirac strings A (vortex type) and B (antivortex type), respectively. (b),(c) Enlargements of the (b) hedgehog and (c) antihedgehog. Gray arrows denote local emergent magnetic fields.
(i) Spin-wave excitations in the quadrupleQ magnetic hedgehog lattices in 3D chiralmagnets: We have theoretically studied thespin-wave modes of the quadruple-Q magnetic hedgehog lattices in 3D chiral magnets[Fig. 1(a)] [1]. First, we constructed a microscopic Kondo-lattice model on the 3D cubic
lattice, which hosts the quadruple-Q hedgehog
lattice spin textures as the ground state.
In this model, interplay of the second- andfourth-order spin-spin interactions mediatedby nested Fermi surfaces is a key to stabilizing
the hedgehog lattice spin textures.
The
hedgehog lattice spin textures in this model
have two types of Dirac strings connecting
a hedgehog [Fig. 1(b)] and an antihedgehog[Fig. 1(c)], the vortex-type strings A andthe antivortex-type strings B. Using thismodel, we studied the spin-wave excitationsof the hedgehog lattice spin textures underan external magnetic field (∥ [001]) by usinglarge-scale adiabatic spin-dynamics simulations based on the kernel polynomial method.
By calculating the microwave absorption
spectra for microwave polarization parallel to
the Dirac strings (i.e. ∥ [001]), we found three
spin-wave nmodes, which are named L1, L2,and L3, in order from the lowest to highest
frequency. These modes are spatially localized
at the Dirac strings and are associated withtranslational motion of the Dirac strings. In
particular, the disappearance of L2 mode,
which is localized at the strings B, is observed simultaneously with the field-induced
hedgehog-antihedgehog pair annihilation at
the strings B.

192

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo(b)

freedom of the honeycomb lattice. This work
will extend the scope of Floquet engineering tomagnetic materials with sublattice degrees of
freedom of magnetic origin.

(a)References[1] R. Eto and M. Mochizuki, in preparation.
[2] R. Eto and M. Mochizuki, in preparation.

Figure 2: (a) Schematic of the three-sublatticeferrimagnetic order. Majority spins form ahoneycomb network. (b) Single-particle spectrum of a Floquet Chern insulator state. Greenlines represent the Floquet states. Numbers
are the Chern numbers of the corresponding
bands.
(ii) Floquet topological phases in the Dirac
half-metallic ferrimagnet: We have theoretically investigated photoinduced topologicalphases in the Dirac half-metallic state in the
spin-charge coupled Kondo-lattice magnets [2].
We here considered the three-sublattice ferrimagnetic order on the 2D triangular latticewhich appears spontaneously when the conduction electron filling is 1/3 [Fig. 2(a)]. We
analyzed this model using the Floquet theory
and found two types of Floquet Chern insulator (FCI) states with gapped spin-polarized
Dirac bands. One is associated with the gap
opening at K point [Fig. 2(b)], whereas theother originates from the gap closing and reopening at M point. The appearance of the former FCI state was also confirmed in our analysis of the periodic Anderson model, from which
the Kondo-lattice model is derived via the perturbational calculation. Both of these FCIstates are induced not by the first-order but by
the higher-order terms in the high-frequency
expansion, and are distinct from conventional
ones induced by the first-order terms associated with the original sublattice degrees of193

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Studies on superconductivity in trestle-type lattices
KAZUHIKO KUROKI
Department of Physics, Osaka University
1-1 Machikaneyama, Toyonaka, Osaka, 560-0043, JapanINTRODUCTION

Most cuprate superconductors possess CuO2
planes, which are considered to play the main rolein the occurrence of superconductivity. One exception is (Sr,Ca)14 Cu24 O41 [1], which consists of
two-leg ladder and chain structures. Yet anotherpossible example is Pr2 Ba4 Cu7 O15−δ [2]. This material is constructed from alternating stacking of
PrBa2 Cu3 O7 and PrBa2 Cu4 O8 , and does have
CuO2 planes, but they are considered to be insulating. There have been suggestions that the doublechain structure in PrBa2 Cu4 O8 is responsible forthe superconductivity, and some theoretical studies support this possibility[3–5]. Quite recently,
there has been stronger evidences that the doublechain structure is responsible for the occurrence ofsuperconductivity[6].

FLEX STUDIES ON THE HUBBARD
MODEL ON A TRESTLE-TYPE LATTICE

The double-chain structure can be modeled by a
Hubbard model on a trestle-type lattice, with intrachain hopping t2 and interchain hopping t1 . Thismodel can further be considered as a single band
one-dimensional system with nearest (t1 ) and next
nearest (t2 ) neighbor hoppings. We theoreticallystudied this model using the ﬂuctuation exchange(FLEX) approximation. We have varied t2 /t1 aswell as the band ﬁlling, and obtained the eigenvalueof the Eliashberg equation at a ﬁxed temperature,
which measures tendency towards superconductivity. Depending on the parameter values, there canbe either two or four Fermi points because the band
structure has a double-well shape. The case where
there are four Fermi points resembles the situation
for the two-leg ladder systems. We ﬁnd that the

eigenvalue of the Eliashberg equation becomes thelargest when the band intersects the Fermi level at
two Fermi points and also barely touches the Fermi
level at two other points. This is in fact reminiscentof the situation where one of the bands is incipientin the two-leg ladder system[7]. Studies applying
other many-body methods to this model are underway in order to verify these FLEX results.

FIRST-PRINCIPLES MODELCONSTRUCTION OF PR2 BA4 CU7 O15−δ

In order to obtain a realistic model of
Pr2 Ba4 Cu7 O15−δ , we have performed ﬁrst principles band calculation of this material using VASP,
and obtained a tightbinding model for the doublechain structure through wannierization. A relatively large t1 /t2 ratio is obtained, consistent withprevious studies[3–5]. Many-body study of this realistic model is now underway. An important point
we are focusing on is (i) the band ﬁlling of the
double-chain structure as a function of oxygen deﬁciency δ and (ii) the origin of the relatively large
t1 /t2 , which is essential in obtaining the tendency
toward superconductivity.

[1] M. Uehara et al., J. Phys. Soc. Jpn. 65, 2764 (1996).
[2] M. Matsukawa et al., Physica C 411, 1011 (2004).
[3] K. Sano, Y.Ono, and Y. Yamada, J. Phys. Soc. Jpn.
74, 2885 (2005).
[4] T. Nakano and K. Kuroki, Phys. Rev. B 76, 014515(2007).
[5] T. Habaguchi et al., J. Phys. Soc. Jpn. 80, 024708(2011).
[6] S. Nishioka et al., Appl. Phys. Exp. 15, 023001(2022).
[7] K. Matsumoto, D. Ogura, and K. Kuroki, Phys.
Rev. B 97, 014516 (2018).

194

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First-principles calculations of dynamical susceptibilities for strongly
correlated materials
Hiroshi SHINAOKA
Department of Physics, Saitama University, Saitama 338-8570
research has been conducted as a collaboration
This report details our recent computational

between Prof. Atsushi Hariki at Osaka City

investigation of SrRu2O6, a material around the

University and the group of Jan Kuneš at TU

boundary between covalent and Mott insulators.

Wien.

Our study aimed to compare the results withthose of inelastic scattering experiments, andfor this purpose, we employed the density
functional theory plus the dynamical meanfield theory (DFT+DMFT) approach. We used
DCore

[1]

and

ALPS/CT-HYB

[2]

for

DFT+DMFT calculations. The codes areparallelized by MPI. We constructed the tightbinding model using Wien2k.

Fig. 1: Temperature dependence of magnetic

We successfully created a phase diagram as a

momentfunction of temperature. Notably, we observed
an antiferromagnetic phase at low temperatures

References

consistent with experimental findings. Figure 1[1] H. Shinaoka, J. Otsuki, M. Kawamura, N.

plots the temperature dependence of the

Takemori, K. Yoshimi, SciPost Phys. 10, 117

magnetic moment.

(2021).

Currently, we are calculating the dynamic[2] H. Shinaoka, E. Gull, P. Werner, Comput.

susceptibility in the low-temperature phase by

Phys. Commun. 215, 128 (2017).

solving the Bethe-Salpeter equation. We expect[3] Diana Csontosová, H. Shinaoka, A. Hariki,

to release our results as a preprint [3].

and J. Kuneš, in preparation.

It is important to acknowledge that this195

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Nonlocal correlation effects in a Hund Ferromagnet
Yusuke NOMURA
Department of Applied Physics and Physico-Informatics, Keio University,
3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan
Understanding

electroncorrelation

to the efficient continuous-time quantum Monteeffects

beyond Fermi liquid theory is a fundamental

Carlo methods developed in Refs. [1-4].

problem in condensed matter physics, since

We find that the Fermi surface expands evenvariousasabove the Curie temperature as if the spin

superconductivity and magnetism emerge frompolarization occurred, contrary to a naïve view

pseudogap and strange-metal states.

that the Fermi surface should recover non-spin-

intriguing

phenomena

suchpolarized behavior. Behind this phenomenon,

A well-known example is the pseudogap
behavior observed in doped cuprates, where

effective“Hund’sstrong nonlocal antiferromagnetic fluctuations

momentumare believed to play an important role. This

correlations induce effective Hund’s coupling

phenomenon has been intensively studied byin momentum space, leading to a large

the cluster extension of the dynamical mean-

modulation of the momentum space occupancy.

field theory.

The reconstruction of the Fermi surface differs

However, nonlocal correlation effects infrom the antiferromagnetic case, where it

multi-orbital systems remain largely elusive. In

creates hot and cold spots in the Fermi surface.

particular, the influence of ferromagnetic

Our finding will give a deeper insight into thefluctuations in the strong-coupling regime isphysics of Hund’s ferromagnets above thestill poorly understood compared to theCurie temperature.

space;

physics”

works

ferromagnetic

in
spin

antiferromagnetic case.
Such a strong-coupling ferromagnetic regime

References

is expected in the multiorbital Hubbard model.

[1] Y. Nomura, S. Sakai, and R. Arita, Phys.

Therefore, we study the two-orbital Hubbard

Rev. B 89, 195146 (2014)

model on the two-dimensional square lattice[2] H. Shinaoka, Y. Nomura et al., Phys. Rev. B

using cellular dynamical mean-field theory.

92, 195126 (2015)

Although the cluster extension in multiorbital[3] Y. Nomura, S. Sakai, and R. Arita, Phys.

systems is computationally expensive, we have

Rev. B 91, 235107 (2015)

managed to accomplish the calculations thanks[4] H. Shinaoka, Y. Nomura and E. Gull,

196

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Comput. Phys. Commun. 252, 106826 (2020)Rev. Lett. 128, 206401 (2022)[5] Y. Nomura, S. Sakai, and R. Arita, Phys.

197

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Transient absorption spectrum in photo-excited
one-dimensional Mott insulators
Takami TOHYAMA
Department of Applied Physics, Tokyo University of Science, Tokyo 125-8585

The elucidation of nonequilibrium states instrongly correlated systems is of great interest since it promises to open a door to theemergence of novel quantum phases. One of
the most signiﬁcant challenges is how to preserve nonequilibrium states, such as the Floquet states, from thermalization, for which therealization of many-body localization may holdthe key. Also, the nonequilibrium-induced
insulator-to-metal transition studied extensively in the one-dimensional (1D) Mott insulator is a fundamental issue associated withcompetition between itinerancy and localization of charge degrees of freedom. Similarly,
non-absorbable terahertz photons with strong
intensity may induce a metallic state via quantum tunneling. Until now it has been commonly accepted that the breakdown of theMott insulators via electric pulses leads tometallic states. However, we raise question
about the validity of this understanding.
To answer this question, in this project
we focus on the possibility of the emergence of novel quantum phases such as glass
phases with intermediate properties between
itinerancy and many-body localization. We
investigate pulse-excited states of the halfﬁlled 1D extended Hubbard model using thetime-dependent density-matrix renormalization group (tDMRG). Our tDMRG makes useof the Legendre polynomial for the calculationof time-evolution operator. We use two target states at a given time t and t + ∆t toconstruct a basis set that can express wavefunctions in the time-dependent Hilbert space.

With the two-target tDMRG procedure, we
can calculate time-dependent physical quantities with high accuracy even when the Hamiltonian varies rapidly with time.
From our tDMRG simulations, we propose
a Mott transition to glassy states induced bymono- and half-cycle terahertz pulses ??. If
we excite the Mott insulating state via photon absorption, we obtain metallic states withlarge Drude weight. In contrast, we ﬁnd thatstrong electric ﬁelds inducing the Zener breakdown strongly suppress the Drude weight evenin the presence of a large number of carriers.
We consider that the Hilbert-space fragmentation due to high ﬁelds leads to glassy dynamics as seen in fracton systems. The glassy
state is accompanied by electric polarizationthat breaks inversion symmetry ??. We also
demonstrate a steady electric current induced
by an ultrashort subcycle pulse. This is due
to an Aharonov-Bohm ﬂux introduced throughthe phase of an electric ﬁeld. Consequently,
time-reversal symmetry is broken. Both symmetry breakings can be monitored by second
harmonic generation. These ﬁndings propose a
new methodology for designing the symmetriesof electronic states and open up a new ﬁeld ofsubcycle-pulse engineering.

References[1] K. Shinjo, S. Sota, and T. Tohyama:
Phys. Rev. Res 4 (2022) L032019.
[2] K. Shinjo, S. Sota, S. Yunoki, and T. Tohyama: arVix:2211.08694.

198

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Research of Three-Channel Kondo Eﬀect
Emerging from Holmium Ions
Takashi HOTTA
Department of Physics, Tokyo Metropolitan University
1-1 Minami-Osawa, Hachioji, Tokyo 192-0397

In 2021, we have discovered the threechannel Kondo eﬀect emerging from Ho ions[1]. Here we brieﬂy report the subsequent development of the research, in particular, themagnitude of the eﬀective impurity spin of thethree-channel Kondo eﬀect.
First we explain the model Hamiltonian. For
the construction of the model, we deﬁne one
f -electron state by the eigenstate of spin-orbit
and crystalline electric ﬁeld (CEF) potential
terms. We obtain Γ7 doublet and Γ8 quartet
from j = 5/2 sextet whereas we ﬁnd Γ6 doublet, Γ7 doublet, and Γ8 quartet from j = 7/2
octet under the cubic CEF potential. Then,
we include the Γ7 and Γ8 conduction electron
bands. Here we consider only the hybridization
between the conduction and j = 7/2 electrons.
The model Hamiltonian is given byH =∑εk c†kµτckµτ +k,µ,τ

+ nEf +∑∑

V (c†kµτ fbµτ +h.c.)

k,µ,τ
†
fjµτ
(λj + Bj,µ )fjµτ

j,µ,τ

+

∑ ∑ ∑
j1∼j4 µ1∼µ4 τ1∼τ4

Iµj11jτ21,jµ32jτ42 ,µ3 τ3 µ4 τ4(1)

× fj†1 µ1 τ1fj†2 µ2 τ2fj3 µ3 τ3fj4 µ4 τ4 ,where εk is the dispersion of conduction electron with wave vector k, ckµτ is the annihilation operator of a conduction electron, fjµτis the annihilation operator of a localized felectron in the bases of (j, µ, τ ), j is the total angular momentum, j = 5/2 and 7/2 aredenoted by “a” and “b”, respectively, µ distinguishes the cubic irreducible representation, Γ8states are distinguished by µ = α and β, whilethe Γ7 and Γ6 states are labeled by µ = γ
and δ, respectively, τ denotes the pseudo-spin,
which distinguishes the degeneracy concerning
the time-reversal symmetry, V denotes the hybridization between f electron in the µ orbitaland conduction electron of the µ band, n is the
local f -electron number at an impurity site,and Ef is the f -electron level to control n.
As for the spin-orbit term, we obtain λa =−2λ and λb = (3/2)λ, where λ is the spin-orbit
coupling of f electron and we set λ = 0.265 eV
for the Ho ions. Concerning the CEF potential term for j = 5/2, we obtain Ba,α = Ba,β =1320B40 /7 and Ba,γ = −2640B40 /7, where B40
denotes the fourth-order CEF parameter forthe angular momentum ℓ = 3. For j = 7/2,
we obtain Bb,α = Bb,β = 360B40 /7 + 2880B60 ,Bb,γ = −3240B40 /7 − 2160B60 , and Bb,δ =360B40 − 3600B60 /7. In the following calculations, we use the parametrization as B40 =W x/15 and B60 = W (1 − |x|)/180 for ℓ = 3,
where x speciﬁes the CEF scheme for the Oh
point group, while W determines the energy
scale of the CEF potentials. In this work, we
set W = 10−3 eV and treat x as the parameterto control the CEF ground state.
For the Coulomb interaction terms, we donot show the explicit forms of I here, but they
are expressed by the four Slater-Condon parameters, F 0 , F 2 , F 4 , and F 6 . These values should be determined from experimental
results, but here we simply set the ratio as
F 0 /10 = F 2 /5 = F 4 /3 = F 6 = U , where U indicates the Hund’s rule interaction among the199

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Simp = log

sin[(2S + 1)π/(nc + 2)],
sin[π/(nc + 2)](2)

where S denotes the local impurity spin andnc indicates the number of channels. In thepresent case with nc = 3, we obtain Simp =log ϕ for both the cases of S = 1/2 and 1.
Thus, in any case, the residual entropy log ϕis considered to be characteristic of the threechannel Kondo eﬀect, but the value of S is notdetermined only from the residual entropy.
To conﬁrm the value of S, we hit upon an
idea to explore a quantum critical point (QCP)
between the three-channel Kondo and Fermiliquid phases. The QCP between the threechannel Kondo and Fermi-liquid phases is expected to be characterized by the residual entropy of the four-channel Kondo eﬀect. Fromeq. (2), we obtain Simp = 0.5 log 3 and log 2 for
S = 1/2 and 1, respectively, for nc = 4.
In Fig. 3(a), we show the f -electron entropies for W = 10−3 and V = 0.7 between
x = 0.65 and 0.7. For x = 0.65, we observe the three-channel Kondo phase, while for
x = 0.7, the local singlet phase appears. At
x = 0.6849, the entropy plateaus with a value
between 0.5 log 3 and log 2, and it eventuallyexits the plateau at around T ∼ 10−10 . We de-

1.5

Simp

1.0

(a) n=10

U=1
l=0.265
W=10-3
V=0.7

0.5

x=0.65x=0.6849
x=0.7

L=8.0
M=5000log 2
0.5 log 3
log f

0.0
-7
-10
-9
-8
-6
-5
-4
-3
-2
-1
10 10 10 10 10 10 10 10 10 10
temperature T [eV]1(b) n=10log 2
0.5 log 3
0.5
log f
U=1
W=10-3l=0.265 V=0.7
0-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1xSimp @T=1.7x10-7

f orbitals. In this work, we set U = 1 eV.
The model is analyzed with the use of a numerical renormalization group (NRG) method.
We introduce a cut-oﬀ Λ for the logarithmic
discretization of the conduction band. Due tothe limitation of computer resources, we keep
M low-energy states. Here we use Λ = 8 and
M = 5, 000. In the NRG calculation, the temperature T is deﬁned as T = DΛ−(N −1)/2 ,where D is half the conduction band width,
which was set as 1 eV, and N is the number ofrenormalization steps.
From the NRG calculations, we have found a
residual entropy Simp of log ϕ with the golden√
ratio ϕ = (1 + 5)/2 for the local Γ5 triplet
ground state. For the multi-channel Kondo effect, the analytic value of the residual entropy
Simp has been found to be given by [2]Figure 1: (a) Entropies vs. temperature for
x = 0.65, 0.6849, and 0.7 with W = 10−3 andV = 0.7. (b) Residual entropies at T = 1.7 ×
10−7 vs. x for −1 ≤ x ≤ 1.
duce this as the signal of the QCP between thethree-channel Kondo and Fermi-liquid phases.
Note that this behavior has also been observed
around at x = −0.64.
In Fig. 3(b), we plot the value of the entropy at T = 1.7 × 10−7 as a function of xfor −1 ≤ x ≤ 1. We ﬁnd sharp two peaks
at around x ≈ −0.646 and x ≈ 0.685, andthe peaks assume values between 0.5 log 3 andlog 2. Since these values are apparently larger
than 0.5 log 3, we deduce that the peaks indicates the QCP characterized by the residual
entropy of the four-channel Kondo eﬀect with
S = 1. This result is consistent with the local
Γ5 triplet ground state.

References[1] T. Hotta, J. Phys. Soc. Jpn. 90, 113701(2021).
[2] I. Aﬄeck and A. W. W. Ludwig, Nucl.
Phys. B 360, 641 (1991).

200

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Magnetic ﬁeld eﬀect on excitonic phase inmulti-orbital Hubbard model
Joji NASU
Department of Physics, Tohoku University
Sendai 980-8578

Excitonic insulators are insulating states
where pairs of an electron in a conduction band
and a hole in a valence band are condensed
coherently. Although this state was proposed
as a superconductivity analog about 50 years
ago, recent discoveries of materials that could
potentially exhibit excitonic insulators have renewed interest in studying this system. Amongthe candidates, cobalt oxides have received signiﬁcant attention due to their multiple spinstates, including low-spin, intermediate-spin,and high-spin states, which are aﬀected by
crystalline ﬁelds and Hund couplings. Recent
research has suggested that excitonic condensation could occur through spontaneous hybridization between distinct spin states. The
theoretical and experimental examinations ofcobalt oxides in this perspective has revealed
the emergence of a magnetic-ﬁeld-induced excitonic phase and spin-splitting. The simpliﬁed two-orbital Hubbard model has been usedin these studies to consider low-spin and highspin states [1, 2, 3].

netic ﬁeld results in the appearance of two excitonic phases [4]. The low-ﬁeld phase is not accompanied by spin-state alignments, while theexcitonic phase appearing higher ﬁeld is associated with a spin-state order and interpreted asa supersolid in terms of excitonic condensation.
The magnetization curve should display signatures of these phases, which is consistent witha recent experiment measuring volume change
up to 600T [4].

In this project, we have investigated magnetic ﬁeld eﬀects on the multi-orbital Hubbard model with the expressive ability of thethree spin states, including the intermediatespin state. We have derived a low-energy eﬀective model and performed mean-ﬁeld calculations, but due to the complexity of the model,
ﬁnding solutions is computationally expensive.
To reduce computation time, we have utilized
the technique of parallel computation. Ourﬁndings show that increasing the applied mag-

[4] A. Ikeda, Y. H. Matsuda, K. Sato,
Y. Ishii, H. Sawabe, D. Nakamura, S.
Takeyama, and J. Nasu: Nat. Commun.
14 (2023) 1744.

References[1] T. Tatsuno, E. Mizoguchi, J. Nasu, M.
Naka, S. Ishihara: J. Phys. Soc. Jpn. 85(2016) 083706.
[2] J. Nasu, M. Naka, S. Ishihara: Phys. Rev.
B 102 (2020) 045143.
[3] J. Nasu and M. Naka: Phys. Rev. B 103(2021) L121104.

201

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Correlation-induced phenomena in Kondo lattice systems
Robert PETERS
Department of Physics,
Kyoto University, Kyoto, 606-8502
correlated

correlations. While energetically low-lying spin

insulators where the resistivity becomes large at

excitations emerge approximately at the Kondo

low temperatures due to a gap in the single-

temperature, which exponentially depends onparticle spectrum [1]. Recently, quantum

the interaction strength, charge correlations

oscillations in strong magnetic fields have been

vanish already at high temperatures.

observed in these materials while insulating.

Furthermore, we analyzed the charge andFurthermore, specific heat and thermal transport

thermal conductivities at finite temperatures bymeasurements on YbB12 and YbIr3Si7 havecalculating the time-dependent current-current

revealed low-lying excitations in these Kondo

correlation functions. While both charge andinsulators that can transport heat but no electric

thermal conductivities can be fitted for all

current [2,3]. Thus, it has been argued that thereinteraction strengths by gapped systems with aare itinerant charge-neutral excitations that are

renormalized band gap, the gap in the system

responsible for thermal transport and lead todescribing the thermal conductivity is generally

observable quantum oscillations in strong

smaller than the system describing the chargemagnetic fields. However, a conclusion to these

conductivity. Furthermore, the maximum of therecent experiments is not yet found.

charge conductivity over the temperature

Using variational matrix product states, wedecreases

analyzed the finite temperature behavior of ainteraction strength. On the other hand, thehalf-filled periodic Anderson model in one

maximum of the thermal conductivity generally

dimension, a prototypical model of a Kondo

lies at smaller temperatures than the maximum

insulator. In our calculations [4], we confirmedof the charge conductivity and follows athe existence of energetically low-lying spin

nonmonotonic behavior , see Fig. 1. We thus

excitations [1] in this model and studied theirfound that charge and thermal conductivities areenergy-momentum dispersion and temperature

affected very differently by correlations anddependence. We demonstrated that charge-

show a different temperature behavior.

Kondo

insulators

arestrongly

charge correlations at the Fermi energy exhibit adifferent temperature dependence than spin-spin

202

monotonicallywithincreasing

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

GQM in the Qi-Wu-Zhang model with andwithout interaction.

References[1] H. Tsunetsugu, M. Sigrist, and K. Ueda
Rev. Mod. Phys. 69, 809 (1997)[2]

Y. Sato, Z. Xiang, Y. Kasahara, T.

Figure 1: Charge conductivity and thermal
conductivity for different interaction strengths. The
insets show the effective gap in both conductivities.

Taniguchi, S. Kasahara, L. Chen, T. Asaba, C.

In another work [5], we introduced a generalized

T. Shibauchi, F. Iga, J. Singleton, Lu Li, and Y.

quantum metric(GQM) on the Brillouin zone for

Matsudacorrelated electron systems. This quantum

Nature Physics 15, 954 (2019)

metric is based on the optical conductivity that[3] Y Sato, S Suetsugu, T Tominaga, Y Kasahara,is written by single-particle Green’s functions.

S Kasahara, T Kobayashi, Shunsaku Kitagawa, K

We analytically proved that this definition is

Ishida, R Peters, T Shibauchi, AH Nevidomskyy,

equivalent to the existing definition of the

L Qian, E Morosan, Y Matsuda, Nature comm.

quantum metric in noninteracting systems and13, 394 (2022)

that it is positive semi-definite as necessary for[4] R Peters and R Rausch, submitted to SciPosta metric. Furthermore, we pointed out the

Physics

relationship between the GQM and the Chern[5] T. Kashihara, Y. Michishita, and R. Peters

number in interacting systems. We thenPhys. Rev. B 107, 125116 (2023)

Tinsman, H. Murayama, O. Tanaka, Y. Mizukami,

numerically confirmed these properties of the203

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Electronic Structures of Nickel Dithiolene Complex-based
Ambipolar Semiconductors
Tomoko FUJINO
Institute for Solid State Physics,
The University of Tokyo, Kashiwa-no-ha, Kashiwa, Chiba 277-8581
Air-stablesingle-component

ambipolar

organic semiconductors that conduct both holes
and electrons are highly desired, but have beenrarely realized. We synthesized neutral nickelbis(dithiolene) complexes (Fig. 1) that fulfill thestringent electronic requirements (i.e., shallow
HOMO levels and deep LUMO levels) for airstable ambipolar semiconductors [1]. The planar

Fig. 1: Nickel dithiolene-based ambipolarstructures

semiconductors.

facilitatedeffectivedenseintermolecular

packingwithinteractions.

Remarkably, changing the methoxy substituentsachieved for a single-component ambipolar

to ethoxy or propoxy groups led to a dramatic

semiconductor material driven in air.

change in the packing mode from onedimensional
maintaining

toherringbone-like,effective

while

intermolecularinteractions, supported by the first-principles

References

band calculation (OpenMX software). They are[1] M. Ito, T. Fujino*, L. Zhang, S. Yokomori, T.

readily solution-processible to form active

Higashino, R. Makiura, K. J. Takeno, T. Ozaki,

layers with well-defined and well-ordered

H. Mori*, J. Am. Chem. Soc. 145, 2127 (2023),

structures in field-effect transistors. Devices

Supplementary Cover.

based on these compounds exhibited efficient[2]. R. Kameyama, T. Fujino*, S. Dekura, S.

ambipolar characteristics, even after several

Imajo, T. Miyamoto, H. Okamoto, H. Mori*, J.

months of exposure to air, whose carrier

Mater. Chem. C 10, 7531 (2022).

mobilityarethe

top-classperformances

204

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Inhomogeneous ground state due to kinetic energy
eﬀect in correlated electron systems
Takashi YANAGISAWA
National Institute of Advanced Industrial Science and Technology (AIST)
1-1-1 Umezono, Tsukuba 305-8568

1

Introduction

Our research is based on the optimized variational Monte Carlo method[1].
We usethe optimized wave function improved by oﬀdiagonal exponential operators such as e−λK
and e−αD (K and D are the kinetic operator and the double occupancy operator, respectively), and the Gutzwiller-Jastrow wave
function where the doublon-holon correlation
is taken into account. We have investigated
the ground-state phase diagram of the twodimensional Hubbard model and the twodimensional d-p model.
We performed parallel computation inMonte Carlo calculations. In order to reduce
statistical errors, we carried out ∼ 500 parallel
calculations. Parallel computing is very important to reduce Monte Carlo statistical errors.
The many-body wave function is written in
the form ψ (1) = exp(−λK)PG ψ0 , where K denotes the kinetic energy part (non-interactingpart) of the Hamiltonian and PG = PG (g) is
the Gutzwiller operator to control the double occupancy with the variational parameterg. ψ0 indicates a trial wave function which
is usually taken as the Fermi sea, the BCS
wave function or the state with some magnetic(or charge) orders. We can improve the wave
function systematically by multiplying by operators PG and e−λK repeatedly. We can consider ψ (2) = exp(−λ′ K)PG (g ′ )ψ (1) for diﬀerent
variational parameters λ′ and g ′ . This wave
function is a very good many-body wave function because the ground-state energy is lowered greatly and the ground-state energy islower than those that are evaluated by anyother wave functions. We also employ the
Jastrow-type wave function which is written

205

as ψJ = PG PQ PJ ψ0 where PJ indicates anearest-neighbor number correlation operator
and PQ controls the nearest-neighbor doublonholon correlation.

2

Ferromagnetic
diagonal
striped states in the Hubbard model

We have performed a variational Monte Carlosimulation to study the ground state of thetwo-dimensional Hubbard model on a squarelattice. The energy gain of possible inhomogeneous electron states are computed as a function of U in the case where the hole density
is δ = 1/8 and next nearest-neighbor transfer
t′ /t = −0.30. The wave function is written asψstripe = PNe PG PJ PDH ψM F ,(1)where ψM F is the eigenstate of the following
Hamiltonian
HM F = −Xijσtij c†iσ cjσ +UX(ρi −sgn(σ)mi )niσ ,
2 iσ

(2)
with ρi = ρ cos(q·(ri −r0 )) and mi = m sin(Q·(ri − r0 )). ρ and m are variational operators. Two incommensurate wave vectors q
and Q characterize the charge and spin conﬁgurations, respectively. We set q = 2Q.
r0 denotes the position of the domain boundary. PNe is the projection operator that extracts only the components with a ﬁxed total number of electrons Ne . PDH = PQ isthe doublon-holon operator given by PDH =Q
Q
i (1 − (1 − η) τ di (1 − ei+τ )) where 0 ≤ η1,
di = ni↑ ni↓ , ei = (1 − ni↑ )(1 − ni↓ ), and τ runs
over all nearest-neighbor sites.

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

In Fig. 1, the energy diﬀerence per site between the normal and stripe states, ∆E =(Enormal − Estripe )/Ns , of various stripe states
at 1/8 doping, is shown as a function of U .
The calculations were carried out on a 16 × 16
lattice. The results show that FM stripe states(open symbols) are more stable than AF stripe
states (ﬁlled symbols) for U/t ≥ 16.
The bond-centered ferromagnetic diagonal
stripe state is stabilized in the strong couplingregion with U/t ≥ 16; this is due to the gainof both kinetic energy and on-site Coulombinteraction energy. The kinetic energy gain
originates from the holon moving over the ferromagnetic domain and the gain of kineticexchange-interaction energy at the antiferromagnetic domain wall. For large U , the gain in
the kinetic energy due to the transfer of holeson the ferromagnetic domain and that of theon-site Coulomb interaction energy exceed theloss of the kinetic exchange interaction energy,
which leads to form an incommensurate electron state with the ferromagnetic domain.

3

Summary

We examined the ground state of the twodimensional Hubbard model by using an advanced variational Monte Carlo method. In
the strong coupling region where U is large, thekinetic energy gain is important in determining
the ground state. In fact, the kinetic energy
gain stabilizes superconducting state for the
optimized BCS wave functions[3]. The kinetic
term also plays an important role in striped
states and the diagonal stripe becomes stablein this region due to the kinetic energy eﬀect.

Figure 1: Optimized energy diﬀerence per site
between the normal and striped states ∆E =(Enormal − Estripe )/Ns as a function of U . The
data are from 16 × 16 lattices where electron number Ne = 224 (1/8 hole doping) andt′ = −0.30. Filled squares indicate the period8 bond-centered AF diagonal stripe state (8period BC-FN-DS). Open squares (circles) denote the bond-centered FM diagonal stripe
state (BC-FM-DS) with period-8 (period-16).
Inset ﬁgure shows the system size dependenceof the optimized energy diﬀerence per site forthe period-8 BC-F-DS state with U = 100 at
1/8 hole doping. The error bars are smaller
than the size of symbols.
T. Yanagisawa, Condensed Matter 4, 57(2019).
[3] T. Yanagisawa, Phys. Lett. A403, 127382(2021).
T. Yanagisawa et al., EPL 134, 27004(2021).
[4] M. Miyazaki, T. Yanagisawa, Phys. Lett.
A448, 128276 (2022).

References[1] T. Yanagisawa et al., J. Phys. Soc. Jpn.
67, 3867 (1998).
T. Yanagisawa, Phys. Rev. B75, 224503(2007) (arXiv: 0707.1929).
[2] T. Yanagisawa, J. Phys. Soc. Jpn. 85,
114707 (2016).
T. Yanagisawa, J. Phys. Soc. Jpn. 88,
054702 (2019).

206

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Unified description of cuprate high-temperature
superconductors using multiband models
Hiroshi WATANABE
Research Organization of Science and Technology, Ritsumeikan University
1-1-1 Noji-Higashi, Kusatsu-shi, Shiga 525-8577

Understanding the various competing
phases in cuprate high-temperature superconductors is a long-standing challenging
problem. Although we have not yet reacheda unified understanding of their properties,
recent studies have shown that orbital degrees
of freedom, both Cu eg and O p orbitals,
are key ingredient. Here we investigate thefour-band d-p model that explicitly includes
the dx2 −y2 , dz 2 , px , and py orbitals [1, 2]. We
construct the tight-binding energy bands for
La2 CuO4 and HgBa2 CuO4 systems based on
the first-principles calculation and examine
the effect of Coulomb interactions with thevariational Monte Carlo (VMC) method. The
Gutzwiller-Jastrow type wave function is used
for the VMC trial wave function. The systemsize for the calculation is N =24×24=576 unit
cells (and thus 576×4=2304 orbitals in total)
and the computation has been done mainlywith the system B of the ISSP Supercomputer
Center. We have compared the variationalenergies of various competing phases including
superconductivity, ferro- and antiferromagnetic phases, and stripe phases with different
periodicities to determine the ground statephase diagram for the hole doping rate x.
We have also calculated physical quantities
such as superconducting correlation functions,charge- and spin correlation functions, andlocal magnetic moments.

with the superconducting correlation function
P dd . In La2 CuO4 , ndz2 increases with x andis strongly enhanced by the Hund’s coupling.
This is because the system tends to increasendz2 to obtain the energy gain from the Hund’s
coupling. Since ndz2 works destructively forsuperconductivity due to its localized character [1, 2], P dd is suppressed by the Hund’s coupling. On the other hand, in HgBa2 CuO4 , suchan increase of ndz2 and a suppression of Pdd
are not so pronounced even for relatively large
Hund’s coupling. It results from the lower siteenergy εdz2 in the HgBa2 CuO4 system thatmakes the dz 2 orbital almost inactive. The difference between two systems are well describedin our four-band d-p model.

References[1] H. Watanabe et al., Phys. Rev. Research
3, 033157 (2021).
[2] H. Watanabe et al., J. Phys.: Condens.
Matter 35, 195601 (2023).

In Fig. 1, we show the hole density of the dz 2
orbital ndz2 as a function of x for several values of Hund’s coupling J/Ud at Ud /t1 =8, along207

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Figure 1: The hole doping rate x dependence of the hole density of the dz 2 orbitalndz2 (solid lines with symbols) and superconducting correlation function P dd (dotted lines)for (a) La2 CuO4 and (b) HgBa2 CuO4 , respectively [2]. Solid (open) symbols correspond to
the superconducting (paramagnetic) phase.

208

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Gap opening mechanism for organic Dirac electron
systems α-(BEDT-TTF)2I3 and α-(BEDT-TSeF)2I3
Akito Kobayashia , Daigo Ohkia , Kazuyoshi Yoshimib and Takahiro MisawabaDepartment of Physics, Nagoya University, Nagoya 464-8602
bInstitute for Solid State Physics, University of Tokyo, Chiba 277-8581, Japan

In the Dirac electron system of organic conductors, phase transitions due to spontaneous
symmetry breaking due to electron-electron interactions have been observed, and this has
attracted interest as a research target for correlated Dirac electron systems. In α-(BEDTTTF)2 I3 , it has been reported that the horizontal stripe charge order with inversion symmetry breaking induces a gap for massless
Dirac electrons [1]. A related compound α(BEDT-TSeF)2 I3 , on the other hand, has adistinctly diﬀerent insulating state from α(BEDT-TTF)2 I3 2 [2]. The gap opening mechanism has not yet been fully clarified.

TSeF)2 I3 , as in the one-dimensional Hubbard
model [4]. Thus we found that α-(BEDTTSeF)2 I3 hosts massive Dirac electrons without symmetry breaking via dimensional reduction [5].

To determine the origin of the diﬀerencesin the gap opening mechanisms in α-(BEDTTTF)2 I3 and α-(BEDT-TSeF)2 I3 , we derive
ab initio low-energy eﬀective Hamiltonians
and solve this using the many-variable variational Monte Carlo method (mVMC) [3]. It
is found that an insulating state with thehorizontal stripe charge order appears in α(BEDT-TTF)2 I3 , which is consistent with experiments and previous studies as shown in
Fig. 1(a). However, in α-(BEDT-TSeF)2 I3 ,we find that an insulating state without any
explicit symmetry breaking is realized. Because of the frustration in the inter-chain magnetic interactions, a dimensional reduction of
the spin correlations occurs. As a result,one-dimensional spin correlations develop in
α-(BEDT-TSeF)2 I3 as shown in Fig. 1(b).
This result suggests that the one-dimensional
spin correlation induces the gap in α-(BEDT-[4] E. H. Lieb and F. Y. Wu, Phys. Rev. Lett.
20, 1445 (1968).

References[1] H. Seo, J. Phys. Soc. Jpn. 69, 805 (2000).
[2] S. Kitou et al., Phys. Rev. B 103, 035135(2021).
[3] T. Misawa et al., Phys. Commun. 235,
447 (2019).

[5] D. Ohki et al., Phys. Rev. B 107, L041108(2023)

Figure 1: Schematic diagrams of α-type organic conductors for (a) α-(BEDT-TTF)2 I3and (b) α-(BEDT-TSeF)2 I3 .

209

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Theoretical study of thermoelectric properties in
Heusler compounds: A weak-coupling approach
Kazutaka NISHIGUCHI
Graduate School of System Informatics, Kobe University
Rokkodai-Cho, Nada-Ku, Kobe 657-8501

Heusler compound Fe2 VAl is a promisingthermoelectric material, which exhibits large
power factor P = σS 2 with σ and S being theelectrical conductivity and Seebeck coeﬃcient,
respectively. On the other hand, the thermal the dimensionless figure of merit ZT =σS 2 T /κ is not so large yet reflecting its large
thermal conductivity κ. Recently, it is experimentally observed in doped Fe2 VAl (i.e.,
Fe2 V0.9 Cr0.1 Al0.9 Si0.1 and Fe2.2 V0.8 Al1−y Siy )as a weekly ferromagnetic material that ferromagnetic fluctuations enhance the thermoelectric properties such as S and P around theCurie temperature (Tc = 285 K). [1] It is not
only a desirable property for practical use inrealistic (room) temperatures, but also a fundamental and intriguing quantum phenomena
where quantum fluctuations and thermoelectric eﬀects are entangled by electron correlations.
Motivated above, we have studied electronic
properties of Fe2 VAl using first-principles calculations based on the density functional theory (DFT) within the generalized gradient approximation (GGA). We performed DFT calculations with the Perdew–Burke–Ernzerhof(PBE) exchange-correlation functional and the
projector augmented wave (PAW) method using The Vienna Ab Initio Simulation Package(VASP) [2] and Quantum ESPRESSO package. [3]Using the maximally localized Wannierfunctions, we could construct a low-energy effective model from the first- principles bands
around the Fermi level. The eﬀective model
is composed of the Fe-3d, V-3d, and Al-2sp3
Wannier orbitals, namely, 19 orbitals. The
DFT b and structure and Wannier orbitals areshown in Fig. 1.
4Band structure
Wannierization

3
2

E−EF (eV)

Heusler compound Fe2 VAl

1
0
−1
−2
−3
−4
W

L

Γ

X

W

K

Γ

k−PathFigure 1: DFT band structure and Wannierization of Fe2 VAl.
Planar Defect in Layered Perovskite
Photocatalyst Y2 Ti2 O5 S2
Layered perovskite Y2 Ti2 O5 S2 (YTOS) is astrong candidate for semiconductor photocatalysts suitable for overall water splitting undervisible light. On the other hand, structuraldefects during synthesis should be controlled
to improve photocatalytic performance similar to other photocatalysts [5]. Very recently,
transmission electron microscopy (TEM) haverevealed extremely large planar defects com-210

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

posed of S–Mg–S layers in YTOS synthesizedusing the flux method.
We investigated the planar defect structureand electronic structure using first-principles
calculations based on the density functional
theory (DFT) [6]. The DFT calculations were
performed within the PBE+U functional using
PAW method, as implemented in VASP [2].
Here we set U = 7.5 eV for the 3d orbitals
at the Ti sites to reproduce the experimental
band gap, 1.9–2.0 eV [5].

we have finally evaluated the defect formationenergy (Ef ). The chemical potentials µi were
determined by the phase diagram of the relevant compounds in the synthesis of YTOS. The
evaluation of the formation energy suggeststhat Ef increases with decreasing µS and/orµMg . Therefore, the optimum conditions for
the S and Mg environments must be optimized
for the synthesis of YTOS, which suppresses
the formation of the planar defect.

References[1] N. Tsujii, A. Nishide, J. Hayakawa, andT. Mori: Sci. Adv. 5, eaat5935 (2019).
[2] G. Kresse et. al.: Phys. Rev. B 47, 558(1993); Phys. Rev. B 49, 14251 (1994);
Computational Materials Science 6 (1996)
15–50; Phys. Rev. B 54, 11169 (1996).
[3] P. Giannozzi et. al.: J. Phys.: Condens.
Matter 21, 395502 (2009) ; J. Phys.: Condens. Matter 29, 465901 (2017) ; J. Chem.
Phys. 152, 154105 (2020).
Figure 2: Planar defect in YTOS is investigated by electron microscopy and firstprinciples calculations.
We have microscopically determined the planar defect structure using the structural optimization, where a Mg atom is centered in
the ab-square intersecting the defect S layers. The obtained defect structure is consistent with the ADF–STEM image. To understand the electronic structures, we have investigated the projected density of states (PDOS)
and band structure, which shows that the defect bands appear within the original band gap.
It is expected that such defect bands considerably increase the electron–hole recombination rate, enhancing the interband transitionsof the defect valence and/or defect conduction bands via radiative or nonradiative transitions, which is an undesirable factor for the efficiency of the overall water splitting of YTOS.
To consider the stability of the planar defect,[4] I. Souza, N. Marzari, and D. Vanderbilt:
Phys. Rev. B 65, 035109 (2001) ; A. A.
Mostofi et. al.: Comput. Phys. Commun.
178 (2008) 685–699; A. A. Mostofi et.
al.: Comput. Phys. Commun. 185 (2014)
2309–2310; G. Pizzi et. al.: J. Phys.: Condens. Matter 32, 165902 (2020).
[5] Q. Wang et. al.: Nat. Mater. 18, 827(2019) ; Z. Pan et. al.: Res. Chem. Intermed. 47, 225 (2021).
[6] M. Nakabayashi, K. Nishiguchi, X. Liang,
T. Hisatomi, T. Takata, T. Tsuchimochi,
N. Shibata, K. Domen, and S. L. Tenno: J. Phys. Chem. C 2023, 127, 7887–
7893 (M.N. and K.N. contributed equallyto this work).

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Nonequilibrium dynamics induced by optical driving
Atsushi ONO
Department of Physics, Tohoku University,
Sendai 980-8578

The ultrafast dynamics of electrons in solids
have been actively studied in recent years.
For example, high-harmonic generation, whichyields harmonics several tens of times higher
than the incident light, has attracted interest
as a phenomenon for generating broadband ultrashort pulses, while its mechanism involves
nonperturbative and nonlinear processes, requiring accurate numerical calculations to understand such intriguing ultrafast phenomena.

energy-band echo, can be observed in various
insulators and reflects the dispersion relationof itinerant quasiparticles.
(a)
0.4

0.0

This year, we focused mainly on thenonequilibrium dynamics of photocarriers excited by a resonant optical pulse and thendriven by an optical electric-field pulse. We
considered theoretical models of band insulators, Mott insulators, and charge-ordered insulators and calculated the real-time evolution using exact diagonalization and infinite
time-evolving block decimation methods on
the ISSP supercomputer system.
We found that in a band insulator, theoptical driving pulse changes the relative
velocity of photocarriers, resulting in carrier
recombination and the generation of echoesof the excitation pulse. A detailed analysis
revealed that the frequency of the echo pulses
reflects the energy dispersion relation of thephotocarriers. Furthermore, we performed
numerical calculations for Mott insulators
described by the Hubbard model and foundthat echo pulses can also be generated despite
the breakdown of the single-electron picture,and their frequencies coincide with the energyof the doublon–holon pairs obtained fromthe Bethe ansatz, as shown in Fig. 1. We
found that this echo phenomenon, termed an

10−2

−0.4
1200
1600
2000

−0.8
−60

−30

10−4 4

0

30

13

60

90

(b) 15

10−2

10
10−3
5

0

0

1

2

−4

10

Figure 1: Echoes in a one-dimensional Mott
insulator [1]. (a) Time profile of the electric
current. The inset shows the Fourier spectrum.
(b) Fourier spectral map of the electric current.
The red curves show the dispersion relations of
the doublon–holon pair. Af is proportional tothe amplitude of the optical driving pulse.

References[1] S. Imai, A. Ono, and S. Ishihara, Phys.
Rev. Research 4, 043155 (2022).

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

3.4

Cooperative Phenomena
in Complex Macroscopic Systems

213

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Development of new tensor-network-based methods andstudy of critical phenomena in quantum/classical magnets
Naoki KAWASHIMA
Institute for Solid State Physics,
The University of Tokyo, Kashiwa-no-ha, Kashiwa, Chiba 277-8581
Tensor-network is now widely recognized as

approximation produces a qualitatively correcta new framework/language for developing

phase diagram, the 2D iPEPS calculation

numerical methods for otherwise intractableyielded a quantitatively accurate one. We also

many-bodytheobserved that iPEPS calculation became slower

structure of the problem from the information-in pinning down the direction of the magnetic

scientific viewpoint. In SY2022, we developed

easy axes as one approaches the phasea few new numerical methods

for studying

boundary --- an indication of the ``easy-axis

quantum spin system in two spatial dimensions

softening''. In fact, the low-energy effective

and classical statistical-mechanical models

theory in the reduced Hilbert space restricted inbased on tensor-network representation. We

the lower atomic levels indicate the asymptotic

then applied them to some typical examples.

U(1) symmetry on the phase boundary, which isproblems

anddescribing

In [1], we studied the square-lattice S=2

consistent with the above-mentioned softening

Heisenberg model with cubic anisotropy. The

phenomena. From these results together withmagnetic anisotropy is quite ubiquitous --- itrenormalization

can be found in virtually every magnet thoughconcluded that the emergence of the continuousin many cases its effect is very weak. However,symmetry

the true nature of critical phenomena can beboundary. Therefore, this criticality belongs todramatically altered by such a weak symmetry

the same universality class as the 3D classicalbreaking field. In spite of the universal nature

XY model. The iPEPS calculation of theof the anisotropy, it is hard to incorporate in thecorrelation functions and other quantities

numerical calculation in an accurate way. We

support this interpretation. Thus, our iPEPS

studied the simplest lattice spin model for the

calculation provided the first systematic andtwo-dimensional quantum ferromagnet withcorrect

cubic anisotropy by means of mean-fieldapproximation, of the phase boundary and theanalysis and tensor network calculation. It

critical phenomena of this simple quantum spin

turnedsystem.

out

that

while

the

mean-field

214

group

becomesanalysis,considerations,exact

beyond

on

the

the

we
phase

mean-fieldActivity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of TokyoIn [2], we studied the phase transitions ofarbitrary two-dimensional lattice can be dealt

the two-dimensional q-state Potts model. While

with by deforming it to a non-uniform square

the universality class of the model is welllattice, though the numerical efficiency of such

established, the scaling function of the Binder

a treatment strongly depends on the original

parameter had not been accurately obtained

problem.

before. In particular, we focused on the nonmonotonic behavior of the Binder parameter.

References

We discovered that for q=3 its non-monotonic[1] Wei-Lin Tu, Xinliang Lyu, S. R. Ghazanfari,behavior as a function of the temperature is not

Huan-Kuang Wu, Hyun-Yong Lee, Naoki

the non-universal properties. Instead it reflects

Kawashima: arXiv:2204.01197.

a bump in the scaling function. Therefore, the[2] Hiroshi Watanabe, Yuichi Motoyama,

non-monotonicity is the universal property of

SatoshiMorita,the Potts model.

Progress

of

and

Naoki

Kawashima,

theoreticalandexperimental

physics 2023, 033A02 (1-19) (2023).

In [3], we developed an open source
software called TeNeS. It is a program package[3]for obtaining ground states of two-dimensional

Kazuyoshi Yoshimi, Satoshi Morita, Takeo

quantum lattice problems. It is based on the

Kato,corner transfer matrix renormalization group

Physics Communications 279 108437 (1-15)(CTMRG) method. In principle, it is designed(2022).

for an arbitrary two-body Hamiltonian and an

215

Yuichi
Naoki

Motoyama,

TsuyoshiKawashima:

Okubo,

Computational

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Large-scale Molecular Simulation of Soft Materials using AllAtom and Coarse-Grained Models
Wataru SHINODA
Research Institute for Interdisciplinary Science,
Okayama University, 3-1-1 Tsushima-naka, Okayama 700-8530
We illustrate here a simulation study onof peptides in different initial configurations.

action of antimicrobial peptide (AMP), melittin,

One system showed lipid extraction and poreon lipid membranes as an example of ourformation by melittin peptides without inducing

research works using massive parallel computer

membrane curvature, while another showed

simulations. In this study, we conducted a series

large pores in a significantly curved membrane.

of coarse-grained molecular dynamics (CG-(Figure 1) This result indicated that, dependingMD) simulations to investigate the complicatedon the melittin arrangements on the membraneactions of melittin in lipid membrane systems. In

at high melittin concentration, pores werea membrane system with a lower peptide/lipid

formed either by relaxation of bending energy(P/L) ratio than the threshold value for melittin

due to large curvature or by the peptide insertion

pore formation, no pores were found during theinto the thinned membrane due to lipid

simulation, although the peptides formed dimers

extraction.

on the membrane. At a higher P/L ratio than theThrough this study, we have gained somethreshold value, we successfully observed pore

insight into the behavior of melittin, an AMP, on

formation caused by the insertion of melittin

lipid membranes. The membrane pore formation

peptides through their N-terminals. During theprocess of antimicrobial activity cannot bepore formation, it was found that membrane

induced by a single melittin peptide, even at P/Lcurvature was locally induced in the vicinity ofratios above the concentration required for pore

the peptide aggregate. These findings wereformation. This was energetically evidenced byconsistent with experimental observations andthe free energy profile of a single melittinsuggested melittin action. Additionally, we

insertion into the membrane. These results were

performed CG-MD simulations for large-scalealso supported by 5 µs CG-MD simulations.

membrane

peptidePore formation is always induced by cooperative

concentrations twice. Interestingly, two different

actions such as induction of melittin membrane

membrane pore formation processes were

curvature and extraction of lipids from theobserved in the systems with the same number

membrane. These two events are the key factors

systems

at

high

216

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Crossover of ﬂuctuating surfactant adsorbed interface
Shunta Kikuchi and Hiroshi WATANABE
Department of Applied Physics and Physico-Informatics, Keio University
Yokohama, Kanagawa 223-8522, Japan

We investigated the mechanical properties ofa surfactant adsorbed at the interface between
two liquids. The restoring force of the membrane is largely due to interfacial tension andelasticity. These are explained by the elastic
energy model by Helfrich with the following
equation [1].
|h(q)|2 =kB T,
2γq + κq 4

where q is the wavenumber, h(q) is the Fouriercomponent of the ﬂuctuation of the membrane,
γ is the surface tension, and κ is the bending
rigidity. The biomembrane is composed of aphospholipid bilayer, and its restoring force isthought to be either interfacial tension or elasticity, but the observed ﬂuctuations are dominated by elasticity, with no eﬀect of interfacial
tension. We therefore investigated ﬂuctuations
in monolayer membranes to determine how theeﬀects of interfacial tension and elasticity contribute to the resilience of the membranes.
We represented two kinds of liquids by
preparing two kinds of atoms. The same typeof atoms interacted with the Lennard-Jones
potential, and the diﬀerent type of atoms interacted with the WCA potential to reproduce a system in which the two liquids arephase-separated. We modeled the surfactant
as a diatomic molecule and prepared an initial state in which the surfactant is adsorbed
at the interface. Then, molecular dynamics
was used for time evolution to observe the ﬂuctuations at the surfactant-adsorbed interface.
LAMMPS was used for time evolution.
Our investigation revealed a signiﬁcant de-pendence of the interfacial tension within thesystem on the surfactant’s natural length.
Modifying the natural length could alter theinterfacial tension from ﬁnite to nearly zero
while maintaining the system’s temperature
constant. The ﬁndings demonstrate that theinﬂuence of q 2 is discernible in the ﬁlm ﬂuctuations when large values characterize interfacial tension. Conversely, a transition from q 2
to q 4 becomes evident in the Fourier spectrum
when interfacial tension approaches near-zerovalues [2]. This crossover implies that whensurfactant molecules are suﬃciently adsorbed
at the interface, and the interfacial tension isnearly zero, the bending stiﬀness dominates in
the restoring force. In contrast, it plays thedominant role when the interfacial tension possesses ﬁnite values.

References[1] W. Helfrich, Z. Natuforsch C28, 693(1973).
[2] K. Shunta and H. Watanabe, J. Chem.
Phys. 158, 124901 (2023).

218

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Correlation between the structure of charged
phospholipid bilayer membrane and ion distribution
Yuji HIGUCHI
Research Institute for Information Technology, Kyushu University
Motooka Nishi-ku, Fukuoka 819-0395

We have studied soft matter by molecularsimulations and revealed the self-assembly processes of amphiphilic molecules [1], water dynamics on lipid bulayer [2], and fracture processes of semicrystalline polymers [3]. This
year, we studied the ion distribution on acharged surface as a model of biomembranes
by coarse-grained molecular dynamics simulation using GROMACS [4] on systems B and C.
The electrostatics in soft matter systems isrelevant to its self-assembly structures anddynamics, and therefore, essential to be understood. In particular, ion distributions ona charged surface as a model of biomembranes containing charged phospholipids havebeen extensively studied. Although theoreticalstudies have revealed the ion distributions perpendicular to the homogeneous charged surface, the ion distributions parallel to the heterogeneous charged surface and their dynamics
are still unclear. Our coarse-grained molecular dynamics simulations show the dynamicalchanges of ion distribution proﬁles on the lipid
membrane during phase separation. Figure 1
shows a typical snapshot of phase separationof neutral and negatively charged lipids with a
NaCl concentration of 100 mM, and the cation
distribution near the lipid membrane at 2.0 µs.
The cations following negatively charged lipids
in microsecond order indicate rapid redistribution of cations parallel to the membrane. We
compare the simulation results with the modiﬁed Poisson–Boltzmann theory and reveal theimportance of the ion-ion interactions in an
aqueous solution. These results will contributeto the future development of the theory.

Figure 1: Phase separation and cation distribution of the charged and neutral lipids mixture at 2.0 µs with a NaCl concentration of 100
mM. The snapshot of the top view of the membrane and cation density (number/nm3 ) near
the surface.

References[1] R. Wakabayashi, R. Imatani, M. Katsuya,
Y. Higuchi, H. Noguchi, N. Kamiya, and
M. Goto: Chem. Commun. 58 (2022) 585.
[2] Y. Higuchi, Y. Asano, T. Kuwahara, and
M. Hishida: Langmuir 37 (2021) 5329.
[3] Y. Higuchi: Phys. Rev. E 103 (2021)
042502.
[4] M. J. Abraham, T. Murtola, R. Schulz, S.
Páll, J. C. Smith, B. Hess, and E. Lindahl,
SoftwareX 1-2 (2015) 19.

219

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Constructing a Dataset of Thermal and Dielectric Properties
of Polymers Using Molecular Dynamics Simulations
Yuta YOSHIMOTO
Department of Mechanical Engineering,
The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656
We have constructed a dataset of thermal

GHz, validating our MD simulation protocol.

and frequency-dependent dielectric properties

Currently, virtual polymers in the PI1M

of polymers using high-throughput molecular

database have been screened using machine

dynamics (MD) simulations. More than 500

learning-aided prediction models of CLTEs andpolymers are constructed from the repeating

dielectric constants to further extend the dataset.

units of previously synthesized polymers [1,2]and virtual polymers in the PI1M database [3]Referencesusing RadonPy [4], an open-source Python[1] L. Chen et al.: npj Comput. Mater. 6(2020) 61.

library. As shown in Fig. 1(a), the MDcalculated values of coefficients of linear[2] S. Otsuka et al.: Int. Conf. on Emerging

thermal expansion (CLTEs) show reasonable

Intelligent Data and Web Technologies

agreement with the available experimental(2011) 22.
[3] R. Ma and T. Luo: J. Chem. Inf. Model. 60

values. Additionally, as shown in Fig. 1(b) and(2020) 4684.

(c), the MD-calculated dielectric constants also
exhibitreasonableagreementwith[4] Y. Hayashi et al.: npj Comput. Mater. 8

the(2022) 222.

experimental counterparts at 10 MHz and 1

Fig. 1: Comparison of the MD-calculated values of coefficients of linear thermal expansion (a),dielectric constants at 10 MHz (b) and 1 GHz (c) with the experimental counterparts [1,2].

220

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Critical Phenomena of Active Brownian Particles
with Uniaxial Self-propulsion
Hiroyoshi Nakano
Institute for Solid State Physics, University of Tokyo
Kashiwa-no-ha, Kashiwa, Chiba 277-8581

Active matter is a distinct class of
nonequilibrium many-body systems that show
unique collective phenomena such as ﬂocking,motility-induced phase separation, giant number ﬂuctuations and active turbulence. Manyexperimental realizations have been reported,
ranging from swimming bacteria to synthetic
colloidal particles, which motivate extensive
investigations of properties of active matter.
Active Brownian particles (ABPs) are one of
the minimal model modeled on biologial motions [1]. The motion of the particles basicallyconsists of three steps: (i) translational random walk, (ii) random rotation of the particle’s orientation, and (iii) propelling movement
along the particle’s orientation. The ABPs undergo a phase separation even when the particles interact with each other via only excluded
volume interactions. Its origin is sought forthe self-propulsion. This type of phase separation is called motility-induced phase separation (MIPS) [2]. The speciality of the origin of
the MIPS motivates the studies of whether themacroscopic behaviors of the MIPS are diﬀerent from those of the phase separation inducedby the intrinsic attractive force, such as theequilibrium liquid-gas transition.
Related to this problem, we focused on the
ABPs with uniaxial self-propulsion [3]. Thismodel is the ABPs that are reﬁned so that theparticles prefer to be oriented along a certain
axis. We particularly examined the propertiesof critical phenomena by using the ﬁnite-size
scaling method.

The result is summarized in Fig. 1. Figure 1(a) presents typical snapshot of the phaseseparated state, and Fig. 1 (b) presents thephase diagram. The phase-separated state appears at the large self-propulsion force F0 . The
critical point is located at the triangle point in
Fig. 1 (b). The binder ratios U for four system sizes are plotted in Fig. 1 (c), which showsthat the binder ratios U cross at the uniquepoint. It implies that this model undergoes
the second-order phase transition. The criticalexponent is estimated as
β = 0.35(4) , νx = 0.65(6).

(1)

Surprisingly, this result is quite close to thecritical exponent of the three-dimensional Isingmodel even though this model is in twodimensions.
Our sophisticated simulations demonstrated
that this result can be understood from thesimilarities between the ABPs with uniaxial
self-propulsion and randomly driven lattice gas(RDLG). For the RDLG, the particles are subjected to the random driving forces along one
axis. It is well known that the universality
class of the RDLG is the same as that of thetwo-dimensional Ising model with the dipolar
long-range interaction, which is close to that of
the three-dimensional Ising model. Our simulations showed that the ABPs with uniaxial
self-propulsion exhibits the same macroscopic
behaviors as the RDLG in the homogeneousstate and the phase-separated state. Based on
this result, we consider that the critical point

221

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Figure 1: (a) Typical snapshot of phase-separated state. (b) Phase diagram of the ABPs withuniaxial self-propulsion. (c) Binder ratios for four system sizes.
of the ABPs with uniaxial self-propulsion belongs to the same universality class as that of
the RDLG [4].
In summary, we performed the simulationsof the two-dimensional ABPs with uniaxial
self-propulsion. We showed that this model
undergoes the MIPS and that the critical behaviors is quite close to the three-dimensional
Ising model. Our result suggested that thecritical points presumably belongs to the same
universality class as that of the RDLG.

References[1] Y. Fily and M. C. Marchetti: Phys. Rev.
Let. 108, 235702 (2012).
[2] M. E. Cates and J. Tailleur: Annu. Rev.
Condens. Matter Phys. 6, 219-244 (2015).
[3] H. Nakano, and K. Adach: in preparation.
[4] K. Adachi, K. Takasan, and K.
Kawaguchi:
Phys. Rev. Research 4013194 (2022).

222

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Numerical Study on Novel Spin Nematic PhaseTôru SAKAI1,2 , Hiroki NAKANO1 , Rito FURUCHI1 ,and Tokuro SHIMOKAWA3
1
Graduate School of Science, University of Hyogo,
Kouto, Kamigori, Hyogo 678-1297, Japan
2
National Institute for Quantum Science and Technology,
SPring-8, Kouto, Sayo, Hyogo 679-5148, Japan
3
Theory of Quantum Matter Unit,
Okinawa Institute of Science and Technology Graduate University,
Onna-son, Okinawa 904-0495, Japan1

Field-induced spin nematic the frustration. This mechanism also had been
Tomonaga-Luttinger liquid prposed for S = 1 antiferromagnetic chain
with the single-ion anisotropy[2].
of the S=1/2 spin laddersystem with anisotropic fer2
Spin Nematic Liquid ofromagnetic rung interaction
the S=1/2 Distorted Diamond Spin Chain in MagThe S = 1/2 quantum spin ladder system withthe anisotropic ferromagnetic exchange internetic Field
action on the rung under magnetic ﬁeld is investigated using the numerical diagonalizationand the density matrix renormalization group(DMRG) analyses[1]. It is found that thenematic-spin-correlation-dominant TomonagaLuttinger liquid appears in some high magnetic ﬁeld. It is included in the TomonagaLuttinger liquid phase where the two-magnon
bound state is realized.
For some suitable parameters, after the ﬁeld-induced phasetransition from this two-magnon TomonagaLuttinger liquid phase to the single-magnon
Tomonaga-Luttinger liquid one, the re-entrant
transition to the two-magnon phase would possibly occur, as the magnetization curves by
DMRG indicate. Several phase diagrams withrespect to the coupling anisotropy, the magnetization and the magnetic ﬁeld are presented.
The present result is a proposal of the candidate system which exhibits the spin nematic
phase without the biquadratic interaction or223

The spin nematic order has attracted a lot ofinterest in the ﬁeld of magnetism. It is a kindof multipole order of spins. The previous theoretical and numerical studies[3] predicted thatthe spin nematic order would be induced by
the frustration of the ferromagnetic and antiferromagnetic exchange interactions, or thebiquadratic interaction. The spin nematic order is characterized by the long-range four spincorrelation and the two-magnon bound state.
The previous numerical diagonalization andthe ﬁnite-size scaling study had indicated thata two-magnon bound state can occur in the
S=1 antiferromagnetic chain with the singleion anisotropy under magnetic ﬁeld[4].The recent analysis of the critical exponents of thespin correlation functions[5] suggested that
this two-magnon bound state includes the spin
nematic liquid phase, as well as the spin density wave (SDW) liquid one. The phase di-Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

agrams with respect to the anisotropy andthe magnetization were obtained by the numerical diagonalization of ﬁnite size clusters.
In the present study[6], the S=1/2 distorted
diamond spin chain is investigated using thenumerical diagonalization of ﬁnite-size clusters. This system is a typical frustrated system. The recently discovered candidate material of this system, K3 Cu3 AlO2 (SO4 )4 called
alumoklyuchevskite[7], includes the ferromagnetic interactions. Thus we investigated the
distorted diamond chain with the ferromagnetic interactions, as well as the coupling
anisotropy. As a result we found that a twomagnon bound state appears in the magnetization process. We also discus about the spin
nematic liquid behaviors.

References[1] T. Sakai, R. Nakanishi, T. Yamada, R.
Furuchi, H. Nakano, H. Kaneyasu, K.
Okamoto and T. Tonegawa, Phys. Rev. B
106 (2022) 064433.
[2] T. Sakai, H. Nakano, R. Furuchi and K.
Okamoto, J. Phys.: Conf. Ser. 2164 (2022)
012030.
[3] T. Hikihara et al., Phys. Rev. B 78 (2008)
144404.
[4] T. Sakai, Phys. Rev. B 58 (1998) 6268.
[5] T. Sakai, H. Nakano, K. Okamoto and R.
Furuchi, J. Phys.: Conf. Ser. 2164 (2022)
012030.
[6] T. Sakai, H. Nakano, R. Fruchi andK. Okamoto, AIP Advances 13 (2023)
015313.
[7] M. Fujihara et al., Sci. Rep. 7 (2017)
16785.

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Magnetization Plateau with Spontaneous Symmetry
BreakingTôru SAKAI1,2 , Hiroki NAKANO1 , Rito FURUCHI1 ,and Tokuro SHIMOKAWA3
1
Graduate School of Science, University of Hyogo,
Kouto, Kamigori, Hyogo 678-1297, Japan
2
National Institute for Quantum Science and Technology,
SPring-8, Kouto, Sayo, Hyogo 679-5148, Japan
3
Theory of Quantum Matter Unit,
Okinawa Institute of Science and Technology Graduate University,
Onna-son, Okinawa 904-0495, Japan1

S=2 Quantum Spin Chain
Translational
Symme- 2
try Broken Magnetization
with the Biquadratic Exchange Interaction
Plateau of the S=2 Antiferromagnetic Chain with The S = 2 quantum spin chain with the singleAnisotropies
ion anisotropy D and the biquadratic exchange

The magnetization plateau of the S=2 antiferromagnetic chain with interaction and singleion anisotropies is investigated using the numerical diagonalization of ﬁnite-size clusters
and some size scaling analyses[1]. The previous
level spectroscopy analysis indicated that two
diﬀerent magnetization plateau phases appear
at half of the saturation magnetization. One isdue to the large-D mechanism and the other isdue to the Haldane one. In the present study
the phase diagram is extended to wider regionof the anisotropies. As a result we ﬁnd anotherhalf magnetization plateau phase, where thetranslational symmetry is spontaneously broken .

225

interaction is investigated using the numerical
diagonalization of ﬁnite-size clusters and thelevel spectroscopy analysis. It is found thatthe intermediate-D phase corresponding to thesymmetry protected topological (SPT) phaseappears in a wide region of the ground statephase diagram. We also obtain the phase diagram at the half of the saturation magnetization which includes the SPT plateau phase[2].

References[1] T. Yamada, R. Nakanishi, R. Furuchi, H. Nakano, H. Kaneyasu, K.
Okamoto, T. Tonegawa and T. Sakai,arXiv:2208.10983; to appear in JPS Conf.
Proc.
[2] T. Sakai, T. Yamada, R. Nakanishi, R.
Furuchi, H. Nakano, H. Kaneyasu, K.
Okamoto and T. Tonegawa, J. Phys. Soc.
Jpn. 91 (2022) 074702.

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Simulation of Quantum Many-Body Systems by
Tensor Network and Sampling
Synge TODO
Department of Physics, University of Tokyo, Tokyo, 113-0033
Institute for Solid State Physics, University of Tokyo, Kashiwa, 277-8581

We developed novel numerical methods by
combining quantum Monte Carlo, tensor network algorithms, and other optimization techniques. We investigated topological quantum
phase transitions in strongly correlated manybody systems and optimization of quantum operations in quantum computing.
Tensor Renormalization Group and Markov
Chain Monte Carlo Methods
The real-space renormalization groupmethod using tensor networks has recently
been widely used as a numerical method
for many-body spin systems. The tensor
renormalization group method can eﬃciently
compute physical quantities for large classical/quantum systems. By extending thetensor network to a form in which tensors
are placed not only on vertices but also onlines connecting vertices, we have increased
the accuracy of the real-space renormalization
method by about 100 times compared to existing numerical methods that require a similar
amount of computation time. This method
applies to general tensor networks. We arealso working on a new sampling method thatcombines tensor network representation andMarkov chain Monte Carlo methods.
Tensor Renormalization Group for Lattice
Fermion Systems
Quantum many-body systems involving lattice fermions appear in various ﬁelds ofphysics. For example, lattice quantum chromodynamics is typical in particle physics, amany-body system describing the interaction

between quarks via gluons. Strongly correlated electron systems in condensed matterphysics are another example of a many-bodysystem consisting of lattice fermions. We develop a method to calculate path integralsof lattice fermion systems. We represent thepath integral of a lattice fermion system bya tensor network and perform approximate
network contraction using the tensor renormalization group to compute the path integral in the inﬁnite volume limit. We extended the bond-weighted tensor renormalization group (BTRG) to handle fermionic systems and performed empirical calculations using 2-dimensional Wilson fermions [1].
Worm Algorithm with Geometric Assignment
MethodIn Monte Carlo methods, there are cases
where there are constraints on the “states”
to be summed, and it is diﬃcult to sample
while satisfying the constraints (e.g., satisﬁability problems). The worm algorithm is amethod for eﬃcient sampling by expanding
the state space in such situations. A worm isa kink (point) that breaks a constraint, andthe idea of the worm algorithm is that theconstraint is violated once and then reconciled later. This Monte Carlo method introduces a constraint-breaking worm (kink), andsampling is performed by stochastically moving the worm. The conventional worm algorithm moves the worm almost wholly at random. We proposed a guideline for moving theworm for eﬃcient computation—moving the226

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Figure 1: Finite-temperature phase diagram ofsquare-lattice quantum dimer model.
worm straight ahead as much as possible—and
conﬁrmed a 25-fold improvement in computational eﬃciency compared to the conventional
worm algorithm [2].
Finite-Temperature Phase Transition of the
Quantum Dimer Model
The quantum dimer model was proposed by
Rokhsar and Kivelson in 1988 as a low-energy
eﬀective model for frustrated magnetic materials. Although the Hamiltonian of the quantum
dimer model has no negative sign problem,
Monte Carlo simulations have been complicated because of the strong geometric restriction on the dimer conﬁguration. We developed
an eﬃcient quantum Monte Carlo method thatcombines a cluster update method based on
the stochastic series expansion (SSE) with afast operator search method using priority
queues. By choosing the appropriate order parameter and ﬁnite scaling, we obtained a ﬁnitetemperature phase diagram of the quantitative
phase diagram of the quantum dimer model.
The critical exponent at the critical point is
consistent with the Kosterlitz-Thouless transition of the classical dimer model. The slope
in the classical limit is also compatible withthe critical temperature of the classical model.
The slope of the phase boundary decreases
as the quantum ﬂuctuations increase, suggest-

ing that the critical temperature becomes zeroat the Rohksar-Kivelson (RK) point, i.e., thecolumnar dimer phase at absolute zero extends
to the RK point without intermediate phases.
Continuous-Space Path-Integral Monte Carlo
The continuous-space path-integral Monte
Carlo method is the only method that cannumerically calculate physical quantities suchas the superﬂuid density and condensation
rate of interacting Bose particle systems. The
worm algorithm allows for simulations in the
grand canonical ensemble of superﬂuid helium
in systems of several hundred particles. However, implementing the algorithm is complex,
involving seven diﬀerent update procedures.
We developed more eﬃcient and simpler update methods in the canonical ensemble. Advanced Markov-chain Monte Carlo methods,
such as Hamiltonian Monte Carlo and event
chain Monte Carlo, have been used to reduce
the autocorrelation time of the simulation further. In addition, the Wang-Landau algorithm
allows the hyperparameters to be automatically adjusted during the simulation, whereas
the conventional worm algorithm has many hyperparameters that signiﬁcantly aﬀect the performance of the algorithm.

References[1] Shinichiro Akiyama, Bond-weighting
method for the Grassmann tensor renormalization group, J. High Energy Phys.
11, 030 (2022).
[2] Hidemaro Suwa, Lifted directed-worm
algorithm, Phys. Rev. E 106, 055306(2022).

227

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Novel phenomena in frustrated magnets
Tsuyoshi OKUBO
Institute for Physics of Intelligence, The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 133-0033

Frustrated interactions often induce novel
phenomena in spin systems. One striking effect when we consider the quantum ﬂuctuation may be the stabilization of quantum spin
liquid without having long-range magnetic order [1, 2]. The ground state of the Kitaev
model on the honeycomb lattice is one of thefundamental quantum spin liquids in the twodimension[3, 4]. In the Kitaev model, the interactions are Ising type, Siγ Sjγ , and the spincomponent γ = x, y, z is determined from thedirection to the neighboring site. Another example is the Kagome lattice Heisenberg model[1, 2]. It has been widely discussed on thepossibility of the gapped Z2 spin liquid or thegapless U (1) spin liquid in the Kagome lattice
Heisenberg antiferromagnets, e.g., see Ref. [5]and references therein.
In this year’s project, we investigated ﬁnite temperature properties of frustrated spinmodels in the two-dimension, particularly concerning the thermal Hall conductivity. We
mainly considered the Kagome lattice Heisenberg model with Dzyaloshinskii-Moriya (DM)interaction and compared its thermal Hall conductivity with the Kitaev model. To numerically calculate the thermal Hall conductivities
at a ﬁnite temperature, we consider severalﬁnite-size clusters with open edges and represent their density matrix as a matrix productoperator (MPO).
We calculate the MPO representing a density matrix at a given temperature by the exponential tensor renormalization grope (XTRG)
algorithm [6]. In XTRG, we calculate the density matrix at an inverse temperature β, ρ(β),

through the relationship ρ(β) = ρ(β/2)ρ(β/2)
starting from the initial condition ρ(β0 ) =exp(−β0 H) ≃ 1−β0 H for the suﬃciently small
β0 , where H is the Hamiltonian of the system.
Once we have an MPO representation of a density matrix at β/2, we can approximate ρ(β)
through a standard tensor network compression algorithm to multiply two MPOs. Whenthe bond dimension of the MPO is D, boththe computation cost and the memory consumption of the XTRG algorithm scale O(D4 ).
Although they are higher than the costs forthe ground state, which scale O(D3 ), we canstill calculate ﬁnite temperature properties by
XTRG.
By the XTRG algorithm, we successfully
calculated the temperature dependence of theenergy current localized at the edge of thecylindrical system for the Kagome lattice
Heisenberg model with the DM interaction under a magnetic ﬁeld. We can calculate thethermal Hall conductivity by taking the temperature derivative of the energy current. In
this system, the direction of the thermal Hall
current depends on the sign of the DM interaction. Interestingly, in the XTRG calculation,
its sign is opposite to the one calculated by
Schwinger Boson mean-ﬁeld theory [7]. We
also found that similar sign inversion in thethermal Hall current is also observed in thecase of the Kitaev model at high temperature:
the sign is opposite to the expectation fromthe linear spin wave theory, although the signat the low-temperature value is consistent withthe expectation from the analysis of the Kitaev
spin liquid. It will be a future problem to un-

228

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

derstand the origin of the diﬀerence in signs inhigh-temperature regions.

References[1] L. Savary and L. Balents, Rep. Prog.Phys.
80 (2017) 016502.
[2] Y. Zhou, K. Kanoda, and T.K. Ng,
Rev. Mod. Phys. 89 (2017) 025003.
[3] A. Kitaev, Ann. Phys. 321 (2006) 2.
[4] Y. Motome and J. Nasu, J. Phys. Soc.Jpn.
89 (2020) 012002.
[5] Y.C. He, M. P. Zaletel, M. Oshikawa,and F. Pollman, Phys. Rev. X, 7 (2017)031020.
[6] H. Li, D.-W. Qu, H.-K. Zhang, et al.
Phys. Rev. Research 2 (2020) 043015.
[7] H. Doki, M. Akazawa, H.-Y. Lee,J. H. Han, K. Sugii, M. Shimozawa,N. Kawashima, M. Oda, H. Yoshida,
and M. Yamashita, Phys. Rev. Lett. 121(2018) 097203.

229

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Theoretical design of novel proteins for medical and industrial
applications
Nao SATO1, Hirotaro SHIMAMURA2, Rina AOYAMA1, Mizuki TERANISHI1,
Koji OOKA3, Runjing LIU1, Kaho KOTANI1, Masataka YOSHIMURA1,
Yuuki HAYASHI1,4, Shunji SUETAKA1, and Munehito ARAI1,2
1
2
4

Department of Life Sciences, Graduate School of Arts and Sciences,

Department of Physics, Graduate School of Science, 3College of Arts and Sciences, andEnvironmental Science Center, The University of Tokyo, Komaba, Meguro, Tokyo 153-8902
Proteins are widely used in medicine and

12 designed peptides, we experimentally foundindustry as therapeutic agents for variousthat a mutant of the MLL TAD peptide had thediseasesindustrial

highest binding affinity for KIX among all

production. However, these proteins have beenpreviously reported inhibitors targeting thedeveloped

experiments,

MLL site of KIX [2]. Thus, our approach may

which require much time and cost. Therefore,

be useful for the rational design of helicalthere is an urgent need to develop efficient andpeptides that inhibit protein-protein interactions

theoreticalinvolved in various diseases.

and

as

throughmethods

enzymes
extensive

for

for

designing

novel

proteins. Here, we theoretically designed a

The programmed death-1 (PD-1) protein on

variety of novel proteins using the Rosetta

the surface of T cells inhibits excessive

software suite [1] and experimentally verified

inflammatory activity of T cells by binding to

the validity of our theoretical designs. In

programmed death ligand-1 (PD-L1) expressed

addition, we constructed a theory that canon antigen-presenting cells. However, reduced

predict the folding mechanism of proteins, toPD-1 function can cause abnormally activatedelucidate the principles of protein design.

T cells to damage normal cells, leading to

The KIX domain of CREB-binding protein

autoimmune diseases. To prevent the T-cellis associated with leukemia, cancer and various

activation by turning on the immune checkpoint,

viral diseases and has attracted considerable

we theoretically designed PD-1 agonists that

attention in drug discovery. We rationallytightly bind the PD-L1 binding site of PD-1.

designed a KIX inhibitor by performing

Using

theoretical saturation mutagenesis to search forsaturation mutagenesis on the fragment of

the peptides expected to bind KIX more tightly

human PD-L1 and selected the mutants that arethan the original partner proteins. Among thepredicted to have high affinity for PD-1.

230

Rosetta,we

performed

insilicoActivity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Experimental verification showed that we have

we are developing a universal method that can

successfully designed the PD-1 binders withgenerally improve the activity of various

higher affinity than the wild-type PD-L1, which

enzymes. We have applied this method to

may be useful as inhibitors of autoimmune

enzymes such as dihydrofolate reductase anddiseases.

aldehyde deformylating oxygenase. Verification

The

novel

coronavirus(SARS-CoV-2)of this method is underway.

infects human cells when its receptor-binding
domain

(RBD)

ACE2.

proteins fold into specific three-dimensional

Antibodies that inhibit this binding have been

structures is necessary to understand the designused as antiviral drugs. However, existing

principles of proteins. To solve this problem,

drugs may become less effective as new

we are developing a statistical mechanics

variants of SARS-CoV-2 with mutations in thetheory that can draw the folding free-energy

RBD emerge. Here, we theoretically modified

landscapes of any proteins [3]. The program isexisting antibody drugs to bind tightly to thewritten

RBD

successfully

mutant

binds

of

to

human

Elucidating the mechanisms by which

SARS-CoV-2

and

in

C++

and

calculated

Python.
the

We havefreeenergyexperimentally showed that the modified

landscapes of folding reactions for all-α and all-antibodies were able to bind tightly to the RBD

β proteins in agreement with experiments. Our

mutant. This approach is expected to be useful

method can be used to draw the free energy

in the future as new viral variants emerge.

landscapes of proteins associated with folding,Formationofaternary

complexbybinding, and catalysis, and to develop new

interleukin 33, its receptor ST2, and IL-1

protein design methods based on the prediction

receptor accessory protein (IL-1RAcP) induces

of protein dynamics.

allergic diseases. To inhibit the complex
formation, we have theoretically designed small

References

proteins that tightly bind ST2 but inhibit the[1] J. K. Leman, et al.: Nature Methods, 17,binding of IL-1RAcP. Experiments to verify the

665 (2020).

designs are ongoing.

[2] N. Sato, S. Suetaka, Y. Hayashi, and M.
Arai: Scientific Reports, 13, 6330 (2023).

Enzymes are widely used in the industrial
production of useful substances. Therefore, it is[3] K. Ooka, R. Liu, and M. Arai: Molecules,

necessary to develop efficient methods to27, 4460 (2022).

improve enzymes. Using the Rosetta software,

231

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Multiscale Flow Simulations on Complex Fluids
Undergoing Phase Transition
Yohei MORII and Toshihiro KAWAKATSU
Department of Physics, Tohoku University, Sendai, Miyagi 980-8578
The knowledge on the properties of complex

irection to the inflow velocity divided by theflows, such as a viscoelastic polymer flow, aroundinflow speed.

an obstacle plays a crucial role in designing flow

Figure 1 shows a snapshot of thechannels in machines. In the present study, we

molecular orientations

analyze a flow of a dilute polymer solution

stretching of the FENE dumbbells around

passing through a moving wing using thea fixed Joukowski wing. When the wing

multiscale simulation platform named MSSP,

starts to rotate slowly changing its angle

where smoothed particle hydrodynamics (SPH)to the inflow direction as 0  45  0 ,method is coupled with microscopic particle

the value of CL changes as shown in Fig.2,

simulations or phenomenological constitutive

where the data are shown for Newtonian (red),

relations.

Maxwellian(green)

and the bond

and

FENE

dumbbell(blue) models, respectively.

We modelled the wing using Joukowski wingWe

as shown in Fig.1, and the polymer chains arecan confirm that the oscillation in CL ismodelled either by a Maxwellian constitutive

suppressed

relation (infinitely extensible) or FENE dumbbells

polymers(Maxwellian) while such a suppression(finite extensible). Simulations were performed at

is less obvious for FENE dumbbells.

byaddingstretchable

Ohtaka, where 2,560,000 SPH particles with5,000,000,000 dumbbell particles embedded in

References

these SPH particles were used. From the[1] Y.Morii and T.Kawakatsu, Phys. Fluids, 33,

simulation data, we evaluated the rift coefficient

093106 (2021).

CL which is defined as the magnitude of theforce exerted on the wing in the perpendicularFig.1 Flow pattern of a polymer solution
around a Joukowski wing. Shown are thedirection (bars) and the extension (colors) ofpolymers chains.

Fig.2 CL as a function of the angle of the wing.

232

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Simulations of quantum-classical-hybrid algorithms forsensor materials with considering noise
Wataru Mizukami
Center for Quantum Information and Quantum Biology, Osaka University, Osaka 560-0043,
Japan
In the fiscal year, a domestically produced

classical computers has become crucial. In thisquantum computer was made publicly available

study, we have performed the development andin the cloud, and IBM announced a quantum

validation

computer with 443 quantum bits. Quantum

algorithms using the ISSP’s supercomputer andcomputers with thousands of quantum bits area quantum circuit simulator rather than an actual

expected to appear in the near future, indicating

quantum computer.

ofquantum-classicalhybridsignificant progress in the development ofThis year, following up on last year's work,

quantum computing. A promising application

we focused on accelerating the simulation of thefor quantum computers is the simulation ofvariational quantum eigensolver (VQE), a

quantum many-body systems. In the field ofrepresentative

materials development, it is expected thatalgorithm. We worked on speeding up VQE

simulations will become more accurate thancomputations using GPUs and implemented

current methods. Research into such simulation

VQE with multi-GPU support.

quantum-classicalhybridMoreover, we have been developing atechniques is gaining momentum.
Our aim is to apply quantum computing tomethod that combines the auxiliary-field

the development of materials for use in sensors.

quantum Monte Carlo (AFQMC) technique with

However, performing practical computations on

VQE to compensate for degrees of freedom thatrealistic quantum chemistry problems with acannot be handled by VQE alone. While alarge number of degrees of freedom using only

combination

quantum computers with limit number of qubits

computation was proposed by Google in 2021,

is not feasible. In addition, current quantum

we have designed and implemented an algorithm

computers have short coherence times, making

that results in a more practical quantum circuit

it difficult to perform useful computations using

than their implementation. We tested this

only quantum computers. As a result, thealgorithm on simple systems, such as the OH

development

molecule,ofquantum-classicalhybridandperformance.

algorithms that utilise both quantum and

233

of

AFQMC

confirmed

and

its

quantum

satisfactory

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Tensor renormalization-group study of random spin
systems
Koji Hukushima
Department of Basic Science, University of Tokyo
3-8-1 Komaba, Meguro-ku, Tokyo 153-8902
Statistical mechanical studies of spin glasses
with both randomness and frustration havebeen conducted for a long time. It is an important issue whether the picture of phase transitions established in the mean-ﬁled spin-glassmodel can be applied to ﬁnite-dimensional
spin-glass models. It is quite diﬃcult to study
the ﬁnite-dimensional spin glass models using analytical methods. Therefore, the main
results for these models have been obtained
mainly by numerical studies, such as Markovchain Monte Carlo (MCMC) methods. However, the long relaxation times of the MCMC
methods for spin-glass systems are a serious
barrier to studying large sizes. Recently, thetensor renormalization group (TRG) method,
numerical calculation methods using tensor
networks have attracted much attention.
In this project, we employ the higherorder tensor renormalization group (HOTRG)
method, one of the TRG-type methods, tostudy the two-dimensional Ising spin-glassmodel. Previous studies have suggested thatthe spin-glass phase transition does not occur
at ﬁnite temperatures in this system when the
distribution of interactions is close to symmetric and highly disordered[1]. We investigate thephase transition of the system near the Nishimori line. Then, the stiﬀness of the system iscalculated for sizes up to the linear dimension
L = 256 using the HOTRG method, which calculates the free energy under diﬀerent boundary conditions. In the calculations at higher
temperatures than the Nishimori line, bothboundary conditions lead to the same transition point and values close to the critical exponent of the pure Ising universality class, thelatter of which is consistent with the picture
dominated by the pure Ising ﬁxed point there,
as pointed out in many previous studies.

In contrast to the phase transitions above
and on the Nishimori line, the transition points
analyzed here have diﬀerent values for thetwist and replica boundary conditions. In
other words, the paramagnetic/ferromagnetic
and paramagnetic/non-paramagnetic transition points are diﬀerent from each other, suggesting the existence of some non-paramagnetic
phase between the paramagnetic and ferromagnetic phases at ﬁnite temperatures in the twodimensional EA Ising model. This may correspond to the spin-glass phase or the previously
proposed random antiphase state. The valueof this transition point at low temperature isroughly consistent with the Nishimori ﬁxed
point, suggesting that the boundary between
the paramagnetic and “spin-glass” phases descends almost vertically from the Nishimori
ﬁxed point toward lower temperatures. Meanwhile, the “spin-glass”/ferromagnetic phaseboundary shows a re-entrant transition.
We also studied the critical phenomena of
the J1 -J2 Ising model by varying the parameter g using HOTRG[2]. This system involves
a competition between the ferromagnetic interaction J1 and antiferromagnetic interaction J2 .
Furthermore, weak ﬁrst-order and second-ordertransitions are observed near the ratio g =J2 /|J1 | = 1/2. Our results, based on HOTRG
calculations for signiﬁcantly larger sizes, indicate that the region of the ﬁrst-order transition
is marginally narrower than that in previous
studies. Moreover, the universality class of thesecond-order transition connected to the transition line is not necessarily fully consistent with
the Ashkin–Teller class considered earlier.
[1] K. Yoshiyama, K. Hukushima: in preparation.
[2] K. Yoshiyama,
K. Hukushima:
arXiv:2303.07733

234

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Molecular dynamics study of phase separationinduced by binding molecules
Koji Hukushima
Department of Basic Science, University of Tokyo
3-8-1 Komaba, Meguro-ku, Tokyo 153-8902
Chromatin is highly folded into a hierarchicalstructure in the nucleus while interacting with
nuclear molecules. In addition, regions of active and repressed gene expression are formedon chromatin. These epigenetic regions are established and maintained by writer moleculesthat write and reader molecules that recognize chemical modiﬁcations on chromatin. The
relationship between the one-dimensional epigenetic pattern on chromatin and the threedimensional chromatin structure is an interesting question that has not yet been elusidated.
The SBS (Strings and Binders Switch)model[1] is a coarse-grained polymer modelthat takes into account the phase separation
between chromatin and nuclear molecules thatbind to chromatin, and to some extent recapitulates the experimental results such as HiC andFISH. In this model, chromatin forms clusters
due to the eﬀective attraction between chromatin segments via molecules that bind multivalently to chromatin[1,2].
In this project, we consider two types ofbinding molecules in the SBS model that promote or repress transcription as playing theroles of reader and writer of epigenetic modiﬁcations. When polymer segments are boundto these binding molecules, the state stochastically changes depending on the type of thebinding molecule.
The binding molecules
switch between two states, one in which they
can bind chromatin and one in which they cannot, at a constant rate, as in the previous
study[3]. In this model, a one-dimensional pattern of epigenetic modiﬁcation appears on thepolymer, accompanied by a three-dimensional
structure.
In this study, we performed coarse-grained
molecular dynamics simulations on the SBS
model with two types of biding molecules.

From the time-series data of the coordination
dynamics obtained by the molecular dynamics
simulations, we devised a method to extract
the time evolution of cluster formation, ﬁssion,coalescence, and annihilation. With the helpof the method, it is revealed that the clusters
move dynamically along the polymer, repeating coalescence and ﬁssion, and that there are
no independent clusters. The evaluation of the
lifetime of the clusters also suggested the existence of a phase that goes to inﬁnity below
a certain nonzero switching rate, despite a ﬁnite switching rate. This can be regarded as adynamical percolation transition.
Our simulations also showed that the sequence of clusters of alternating types andthe hierarchical structure of these clusters on
the chromatin polymer emerge with the two
types of binding molecules. When the binding molecules do not switch states, the polymer takes on a sausage-like shape with hierarchical structure, and as the switching rate increases, the structure becomes more dynamic
and the clusters become more independent ofeach other. This provides insight into themechanisms underlying the dynamics and stability of one-dimensional epigenetic patterns
and three-dimensional cluster structures onchromatin in the SBS model.
The present work has been done in collaboration with Ryo Nakanishi.
[1] Mariano Barbieri et al., PNAS, 2012 109(40) 16173-16178.
[2] Andrea M. Chiariello et al., Sci. Rep., 2016
29775(6).
[3] Chris A. Brackley et al., Biophys. J., 2017112(6) 1085-1093.

235

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Numerical simulations combined with machinelearning to search for magnetic skyrmion in
frustrated magnets
Satoru Hayami
Graduate School of Science, Hokkaido University, Sapporo 060-0810, Japan

A magnetic skyrmion, which is characterized by a topologically-nontrivial swirling spin
texture, has attracted considerable attention,
since it leads to unconventional transport phenomena, such as the topological Hall eﬀect. In
order to realize new electronic and spintronic
devices based on the skyrmion, it is highly desired to understand when and how such topological spin textures are stabilized from the microscopic viewpoint. In the project with number 2022-Ca-0002, we have numerically investigated the possibility of magnetic skyrmions byfocusing on magnetic frustration mechanisms
in localized and itinerant systems. We havepresented the main results this year below.
(i) Skyrmion crystals in trilayer systems:
We have theoretically investigated a new stabilization mechanism of the skyrmion crystals ina trilayer system [1]. By focusing on the roleof the layer-dependent Dzyaloshinskii-Moriya
interaction and performing the simulated annealing for the spin model, we obtained various types of skyrmion crystal phases with different skyrmion number in a low-temperaturephase diagram. We found the skyrmion crystal phases characteristics of the layered system:
the twisted surface skyrmion crystal, antiskyrmion crystal, and high-topological-number
skyrmion crystal phases.
(ii) Rectangular and square skyrmion crystals in centrosymmetric tetragonal magnets:
We considered the instability toward the rectangular skyrmion crystal without fourfold
rotational symmetry on a centrosymmetricsquare lattice with magnetic anisotropy [2].
The model includes the eﬀect of frustrated exchange interactions and the easy-axis singleion anisotropy. We have shown that a competition of the interactions in momentum space
triggers the formation of both rectangular andsquare skyrmion crystals. We have also shownthat a phase transition between them occurs
while varying the amplitude of the magnetic
ﬁeld.
(iii) Skyrmion crystals in a nonsymmorphic lattice structure: We have numerically investigated the stabilization mechanism of theskyrmion crystals in a nonsymmorphic latticesystem with screw symmetry [3]. By performing unbiased numerical simulations basedon the simulated annealing for a layered spinmodel to be characterized as a nonsymmorphic system with a threefold screw axis, werevealed that the skyrmion crystal is stabilized in both zero and nonzero magnetic
ﬁelds by the interplay between the ferromagnetic interlayer exchange interaction and thelayer- and momentum-dependent anisotropic
exchange interactions. We also found two new
types of skyrmion crystals characteristics of athreefold-screw system. Our result indicateda possibility to realize the skyrmion crystal in
nonsymmorphic systems without a solely rotational symmetry within the same plane, whichleads to extending the scope of the skyrmion
crystal-hosting materials.
(iv) Skyrmion crystals in a ferromagnetic/antiferromagnetic bilayer: We proposed236

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyoa new stabilization mechanism of the skyrmion
crystal in a bilayer system, which is composedof ferromagnetic and antiferromagnetic layers
but free from the Dzyaloshinskii-Moriya interaction [4]. By performing the variational calculation and Monte Carlo simulation for the frustrated spin model, we found that the skyrmion
crystal is robustly stabilized from zero to ﬁnite temperatures for the strong interlayer coupling. The results provided a new scenario torealize the skyrmion crystal in multi-layer systems including the domain structure and heterostructure by making use of the layer degreeof freedom.
(v) Skyrmion crystal in the Hubbard model:
We investigated a realization of the skyrmion
crystal in the centrosymmetric triangularlattice Hubbard model without localized
spins [5]. On a basis of the self-consistentmean-ﬁeld calculations, we found two types ofskyrmion crystals with spatially nonuniform
charge modulations in the ground state at azero magnetic ﬁeld. We have also shown thatan unconventional triple-Q vortex phase without the net spin scalar chirality emerges. Furthermore, we found that the magnetic ﬁeld induces the topological phase transition from the3Q vortex phase to another skyrmion crystal
phase. We clariﬁed that the microscopic originof these phases is the site-dependent moment
reduction due to the kinetic motion of electrons
in the Hubbard model, which is qualitatively
diﬀerent from the situation in the Kondo lattice model and other spin models.
(vi) Various topological spin textures in an
itinerant honeycomb magnet: We have theoretically revealed new types of topological spin
textures consisting of the magnetic skyrmion
by focusing on the sublattice degree of freedom in the lattice structure [6]. We found ﬁve
types of skyrmion crystals with diﬀerent topological properties from the conventional one in
an itinerant honeycomb magnet based on thesimulated annealing. We have shown that thesublattice degree of freedom in the itinerantmagnet enables us to realize the unobserved
skyrmion crystals, such as the antiferro-typeskyrmion crystal and ferri-type skyrmion crystal.
(vii) Magnetic ﬁeld–temperature phase diagrams for centrosymmetric tetragonal magnets: We systematically constructed the magnetic ﬁeld–temperature phase diagrams of theeﬀective spin model for the itinerant electron
model on a square lattice on the basis of an efﬁcient steepest descent method with a small
computational cost [7]. We found two distinct mechanisms to stabilize the square-typeskyrmion crystal from zero to ﬁnite temperatures: One is the positive biquadratic interaction and the other is the high-harmonic
wave-vector interaction. By showing the phase
diagrams for diﬀerent sets of model parameters, we discussed the stability tendency of theskyrmion crystal and other multiple-Q states.

References[1] S. Hayami, Phys. Rev. B 105, 184426(2022)[2] S. Hayami, Phys. Rev. B 105, 174437(2022).
[3] S. Hayami, Phys. Rev. B 105, 224411(2022).
[4] K. Okigami, R. Yambe, and S. Hayami, J.
Phys. Soc. Jpn. 91, 103701 (2022).
[5] K. Kobayashi and S. Hayami, Phys. Rev.
B 106, L140406 (2022).
[6] R. Yambe and S. Hayami, Phys. Rev. B
107, 014417 (2023).
[7] S. Hayami and Y. Kato, J. Magn. Magn.
Mater. 571, 170547 (2023).

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Universality classes of 3D ±J Ising model
Y. Ozeki and Y. Terasawa
Graduate School of Informatics and Engineering, The University of Electro-Communications

We investigate the universality classes of the ±J
Ising model in three dimensions. Together with theferromagnetic (FM) classes obtained previously [1],the spin glass (SG) transition is analyzed by the useof dynamical scaling analysis for nonequilibrium relaxation (NER) process [2]. the p-dependence ofSG transition temperature TSG(p) and critical exponents γ, ν, z are obtained.
To estimate the dynamical exponent z precisely, an extrapolation scheme is introduced forthe asymptotic value of logarithmic derivative ofthe dynamical Binder parameter at the transition
point. To do this, we performed Monte-Carlo simulation for multi-replica systems. Each replica system has the same bonds Jij , while the initial configuration of relaxation is distinct. The replica overlap q AB (t) between two replicas A and B at timet, is defined by:
1 X (A)(B)
q AB (t) =S (t)Si (t),
(1)
N i i
(A)

where Si is the i-th spin variable in replica A attime t. Te SG Binder parameter is obtained by!
1
q (4) (t)gSG (t, T ) =3 − (2),(2)
2{q (t)}2
where
q

(k)

2
(t) =
n(n − 1)

"

X
A>B

{q

AB

(t)}

k

#

.

(3)

J

When T = TSG , this function is expected to diverge
asN × gSG (t, T ) ∼ td/z
(4)where d is the spacial dimension. This relation allows us to estimate z independently.
The SG region in 0.5 ≤ p ≤ pmc is analyzed. To
estimate TSG and zν, we perform the dynamicalscaling analysis for SG susceptibility χSG (t) withthe linear size L = 61, 512 independent samples
and 64 replicas are used for averaging. To estimatez, we calculate gSG (t) with the linear size L = 31,
460800 independent samples and 64 replicas areused for averaging.
In Figs. 1, the resulting p-dependence of SG critical exponent ν on the PM-SG boundary are shown

238

Figure 1: Result of estimated ν.
together with the previously obtained FM exponents on the PM-FM boundary [1]. Since both the
FM and SG transitions occur at the multi-criticalpoint (MCP), and FM and SG exponents become
identical with each other [3], one may survey an
exponent in whole region including SG and FM
phases. As for the exponent ν, there exist three
FM universality classes associated with the nonrandom FM fixed point (p = 1), the random fixed
point (pmc < p < 1), and the MCP (p = pmc ) [1].
On the other hand, the present result indicates asingle universality class for PM-SG transition in
0.5 ≤ p < pmc , and ν takes a value ∼ 1.55.

References[1] Y. Ozeki and N. Ito, J. Phys.: Math. Theor.
40 R149 (2007).
[2] Y. Echinaka and Y. Ozeki, Phys. Rev. E94
043312 (2016).
[3] Y. Ozeki and N. Ito, J. Phys.A: Math. Gen.
31 5451 (1998).

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Localization-delocalization transition in
non-Hermitian random system
TOMI OHTSUKI1
TOHRU KAWARABAYASHI2
KEITH SLEVIN3
1) Dept. Phys., Sophia University, Chiyoda-ku, Tokyo 102-8554, Japan
2) Dept. Phys., Toho University, Miyama 2-2-1, Funabashi 274-8510, Japan
3) Dept. Phys., Osaka University, Toyonaka, Osaka 560-0043, Japan

Random quantum systems lack translational/rotational symmetries but can exhibit
time reversal, spin rotation, chiral, andparticle-hole symmetries. These symmetries
classify random quantum systems into 10 distinct symmetry classes. The Wigner-Dyson
classes discovered in the 1950s and 1960s haveundergone signiﬁcant research. Recent experimental ﬁndings regarding topological insulators and Weyl semimetals have inspired researchers to explore random systems outsideof the Wigner-Dyson classes. These newly
discovered 7 classes [3 chiral classes (GadeWegner classes) and 4 Bogoliubov-de Gennes
classes] exhibit discrete chiral or particle-holesymmetries.
We have studied a series of Hamiltonians
that pertain to the newly discovered quantum
classes. We then introduced non-Hermiticity
to the Hamiltonian, and we were able to extend the classiﬁcation of systems to encompass 38 distinct classes. Through our research, we demonstrated that the inclusion of
non-Hermitian elements alters the universality
class [1] and that the widely utilized transfer
matrix method employed in Anderson transitions also applies to non-Hermitian systems [2].
Additionally, our investigations revealed thatthe critical behaviors of the 38 non-Hermitianclasses can be mapped to universality classes
for the 10 Hermitian symmetry classes [3] (seeFig. 1).
Condensed matter physics has seen a signiﬁcant increase in the utilization of machinelearning techniques. Our research involves

using convolutional neural networks (CNN)
to analyze wave functions and study various
quantum phase transitions. Speciﬁcally, we
employ this method to classify wave functionsin quasi-periodic systems[4]. Moreover, we utilize the neural network to interpret the quantum ﬁngerprints in mesoscopic systems [5].
References
1. X. Luo, T. Ohtsuki, R. Shindou, Physical Review Letters 126, 090402 (2021).
2. X. Luo, T. Ohtsuki, and R. Shindou, PhysicalReview B 104, 104203 (2021).
3. X. Luo, Z. Xiao, K. Kawabata, T. Ohtsuki, and
R. Shindou, Physical Review Research 4, L022035(2022).
4. X. Luo, T. Ohtsuki, Physical Review B 106,
104205 (2022).
5. S. Daimon, K. Tsunekawa, S. Kawakami, T.
Kikkawa, R. Ramos, K. Oyanagi, T. Ohtsuki, E.
Saitoh, Nature Communications 13, 3160 (2022).

Figure 1: Correspondence of the Andersontransitions in non-Hermitian and Hermitiansystems.

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Slow spin dynamics near phase boundary between
diﬀerent ordered phases
Hidemaro SUWA
Department of Physics, University of Tokyo, Tokyo 113-0033

The metastable state is ubiquitous in ourdaily lives and in condensed matter physics.
A typical example of the metastable state isdiamond, a solid form of carbon. Graphite is
the most stable phase at room temperature,
but diamond is also stable as a metastablestate, indicating that the time scale of relaxation is much longer than that of experiments.
The metastable state is essential to not only
statistical mechanics but also several functional materials. For example, the use of themetastable state enables higher-density storage and broader applications in phase-change
memory. Recently, a skyrmion-lattice phase of
MnSi has been stabilized by rapid cooling as ametastable state in a wider region of the phase
diagram.
What is the condition for a metastable state
to stay for a long time, or in what situation do
the relaxation dynamics become signiﬁcantly
slow? We can ﬁnd some answers in glass systems. Slow dynamics typically emerge near a
eutectic point, a triple point between a liquid,a crystal, and another crystal phase. In the dynamics of particle systems, the particle number
is usually conserved in time evolution. On theother hand, the dynamics of spin systems are
typically non-conserved: magnetization is notconserved in time evolution. The question weanswer through this project is where slow dynamics appear in the Ising model, a prototype
model of statistical mechanics.
In analogy to the eutectic point, we derived
one of the simplest Ising models showing a“Y-shape” ﬁrst-order phase boundary (Fig. 1):

the J1 -J2 -J3 Ising model with a four-spin interaction. The four-spin interaction could be
induced by integrating out other degrees of
freedom, mimicking an eﬀect of interactions
with charge, phonons, etc. We studied thequench dynamics of the Ising model using theMonte Carlo method on ISSP System B in theclass C project (ID: 2022-Ca-0100). The initialstate is a random spin conﬁguration, and thetime evolution is simulated by Glauber dynamics. The relaxation time becomes signiﬁcantly
longer near the phase boundary. We also studied snapshot conﬁgurations and the structure
factor as a function of time. Near the phaseboundary, clusters of the metastable state remain for a quite long time and prevent vortices
from recombining and annihilating. Our simulation is consistent with experimentally observed slow dynamics in vanadium oxides. The
result of this project sheds new light on thestability of the metastable state and slow dynamics emerging near a phase boundary.

Figure 1: Schematic Y-shape phase diagram of
the Ising model. The phase boundaries are of
the ﬁrst order.

240

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Polymer Dynamics under Elongational Flow
Takahiro MURASHIMA
Department of Physics, Tohoku University,
Aramaki-aza-Aoba, Aoba-ku, Sendai, Miyagi 980-8578
Fundamental studies of polymer dynamics
under elongational flows are important for abetter understanding of complex flow in themolding process. We have developed a uniform
elongational flow method [1] that can handle
very

large

deformations

underarbitrary(uniaxial, planar, and biaxial) elongationalflows. This code works on LAMMPS [2],which brings the benefits of massive parallelFig. 1: Supplementary Journal Cover ofMacromolecules, Volume 55, Issue 21 [4].

computation at ISSP. Our previous work [3]was the first to discover the stress overshootphenomena of ring/linear blends under biaxial

References

elongational flow using this method. In this[1] T. Murashima, K. Hagita, T. Kawakatsu,

project,

multicyclic/linear

Nihon Reoroji Gakkaishi, 46, 207 (2018);

blend systems under biaxial elongational flows.

https://github.com/t-murash/LAMMPS-UEFEX

We found sharper overshoot phenomena of[2] S. Plimpton, J. Comp. Phys. 117, 1 (1995);

multicyclic/linear

A. P. Thompson et al., Comp. Phys. Comm.,

weinvestigated

blend

than

those

of

monocyclic/linear blend in our previous work271, 10817 (2022); https://www.lammps.org[3].

[3] T. Murashima, K. Hagita, T. Kawakatsu,

The

originofovershootinmulticyclic/linear blend was the open-to-closed

Macromolecules, 54, 7210 (2021).

topological transition. This work [4] was[4] T. Murashima, K. Hagita, T. Kawakatsu,

selected as a supplementary journal cover ofMacromolecules, 55, 9358 (2022).

Macromolecules as shown in Fig. 1.

241

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Stability of quantum spin liquids in frustrated spin systems
Kota IDO
Institute for Solid State Physics,
The University of Tokyo, Kashiwa-no-ha, Kashiwa, Chiba 277-8581
model with other perturbative terms under the

The Kitaev spin liquid has attracted attention

magnetic field along arbitrary direction.

in order to realize topological quantum
computing by Majorana fermions [1, 2].

In this project, we analyze the magnetization

However, because Kitaev candidate materials

process of the Kitaev-Heisenberg model under

have additional magnetic interactions such as

the magnetic field along [001] or [111] direction

the Heisenberg term, the ground state becomes aby using the generalized spinon pair-productmagnetic ordered state. Applying a magnetic

wavefunction, which has been already used forfield is a promising way to suppress theanalysis of quantum spin liquids in strongly

magnetic order. In fact, it is reported that thecorrelated systems [4]. We find that the VMC

half-integer quantization of the thermal Hall

results by using this trial wavefunction can wellconductivity has been measured in a Kitaev

reproduce the exact diagonalization results evencandidate material under a magnetic field, which

when we impose a sublattice structure on thesignals the realization of the Kitaev spin liquid

trial wavefunction. Applications to large size

by applying the magnetic field [3]. Therefore, itsystems will be reported elsewhere.

is desirable to accurately solve the extended
Kitaev model under a magnetic field.

References[1] A. Kitaev, Ann. Phys. 321, 2 (2006).

The variational Monte Carlo (VMC) method
is a numerical method applicable to analysis of[2] H. Takagi et al., Nat. Rev. Phys. 1, 264

two-dimensional systems [4, 5]. Recently, by(2019).

introducing the Jastrow-BCS wavefunction[3] Y. Kasahara et al., Nature 559, 227 (2018).

constructed from the Jordan-Wigner fermions,[4] M. Kurita, Y. Yamaji, S. Morita, and M.

we succeeded in analyzing the magnetization

Imada, Phys. Rev. B 92, 035122 (2015).

process of the Kitaev honeycomb model with

[5] T. Misawa et al., Comput. Phys. Commun.

high accuracy [6]. However, the application of235, 447 (2019).

this approach is limited to only the Kitaev model[6] K. Ido and T. Misawa, Phys. Rev. B 101,with the magnetic field along z-direction. Thus,

045121 (2020).

it is still challenging problem to solve the Kitaev

242

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Efficient Sampling Simulation of
the Soft Modes Significantly Contribute to Protein Properties
D. P. TRAN, K. TAKEMURA, T.N. WIJAYA, R. OSAWA, M. YOSHIOKA, H.
NAKAYA, S. IKIZAWA, T.Y. CHU, H. T. NGUYEN and A. KITAOSchool of Life Science and Technology, Tokyo Institute of Technology
Ookayama, Meguro, Tokyo 152-8550
In this project, we employed Parallel(Dist1) and Ala146 and Val286 (Dist2) andCascade Selection Moelcular Dynamics

plot in Figure 1. We found that the FEL ofsimulation (PaCS-MD) [1] as the enhanced

CALB/W

sampling method to conduct efficientspace than that of the CALB/I, because ofmolecular simulation. Recently, we used thethe flexibility of the lid region in water. The

conmbination of the PaCS-MD together

minima i1 and i2 in CALB/I FEL are stiffer

with Markov State Modeling (MSM) to

comparing to that of w1-3 in CALB/W FEL.

unveil the energetic and kinetics of the

The free energy level of the closed

interfacial activation of Candida antarctica

conformation is much lower than that of the

Lipase B (CALB) [2].

open conformation in water.

spanslargerconformational

We first constructed the well-relaxed
structures of the CALB/W in water and theinterface of water and tricaprylin (CALB/I).
After that, we performed the PaCSMD/MSM from open-to-close and close-toopen for both CALB/W and CALB/I with30 replicas. Each trajectory length is 0.1ns.

Figure 1. Free energy landscape (FEL) of

We monitored the the α5 helix (residues

CALB/W and /I obtained by PaCS-

142‒146) and half of the α10 helix

MD/MSM(residues278‒287)

inall

PaCS-MD
After that, we performed the PCCA++

simulations.

[3] to explore macroscopic view of theThen we constructed the free energy
landscape (FEL) of both systems by using

open-closetransition

Markov State Modeling from the inter-Cα

employed

thedistances between Arg309 and Leu144

microstates into 6 microstates for both

243

of

coarseCALB.

We

grainingtheActivity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

cases. The CALB/W conformation can bei) small and hydrophilic substrates bindsplit into three states: closed states (WC1,without interfacial activation, ii) large andWC2, and WC3), one semi-open state (WS),bulky substrates bind via interfacial activation.

and two open states (WO1 and WO2). In

These findings could help expand CALBCALB/W, we found that the transition fromapplications towards a wide variety ofsemi-open state to the closed state is one-

substrates.

order of magnitude faster than that to theopen state. In contrast, the CALB/I exhibits

References

the timescale of semi-open to open states in[1] R. Harada and A. Kitao. J Chem Phys 139,

nanosecond order. CALB prefers the035103 (2013).

transition to the closed state in water but[2] T. N. Wijaya, A. Kitao (2023), ChemRxiv.

moves to the open state in the interface.

doi: 10.26434/chemrxiv-2023-17v42[3] S. Roeblitz, M. Weber, Adv Data Anal
Classif 7, 147 (2013)

Figure 2. Microstates assignment using
PCCA++ for both CALB/W and /I. PCCA++
results and MFPT between macrostates for
CALB/W(left)andCALB/I(right).

Transition time and free energy difference arealso indicated.
Via analysis of PaCS/MSM, we found thatthe closed state is the most stable in the water
system but the stable conformation in theinterface system shifts to the semi-open state.
In the interface system, the transition
probability to the open state is higher than in
the water system. We also suggest double
mechanisms of substrate binding as followed:

244

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Thermal effects on quantum frustrated magnetisms
Tokuro Shimokawa
Theory of Quantum Matter Unit, Okinawa Institute of Science and Technology Graduate
University, Onna 904-0495, Japan
Investigating the effects of thermal fluctuations

We also report on the investigation of the staticin quantum frustrated magnets is crucial forand dynamic properties of the S=1/2 JA-JB-JC

developing our understanding of quantum spin

honeycomb-lattice Heisenberg model, which is

liquids. Ca10Cr7O28 (CCO) [1] is a quantum

believed to be important for understanding thespin liquid candidate that can be described byquantum spin liquid behavior in different

the S=1/2 bilayer breathing kagome (BBK)honeycomb-lattice material Cu2(pymca)3(ClO4)

Heisenberg model. Here, we examine the[3]. The investigations were carried out using

importance of thermal fluctuations by focusing

quantum Monte Carlo simulations based onon

ALPS application [4] and exact diagonalization.

the

J1-J2

Heisenberg

classical

modelhoneycomb-latticecorrespondingto

Our results demonstrate the realization of athequantum paramagnetic state in this material,classical system of this S=1/2 BBK model.

and suggest that focusing on dynamic properTo do this, we employ classical Monte Carloties is crucial for experimentally identifying

simulations, utilizing a hybrid parallelization

this state [5].

approach combining the heat-bath method,
over-relaxation method, and replica exchange

References

method. We investigate the ordering behavior[1] C. Balz, et al. Nat. Phys. 12 942 (2016).

of the honeycomb lattice model in a finite-[2] T. S., et al, in preparation

temperature magnetic field with a focus on the[3] Z. Honda, et al, J. Phys. Soc. Jpn. 84,

several034601 (2015).

J2/J1

parameter

values.

Physicalquantities, including specific heat, magnetic[4] A. Albuquerque, et al, J. Magn. Magn.

susceptibility, equal-time structure factor, andMater. 310, 1187 (2007).

chirality, are examined to identify the presence[5] T. S., Ken'ichi Takano, Zentaro Honda,

of a Skyrmion lattice state with sublattice

Akira Okutani, and Masayuki Hagiwara, Phys.

structure and other additional multiple-q states

Rev. B 106, 134410 (2022).

[2].

245

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Matrix Product Renormalization Group
Masahiko G. YAMADAAB
Masahiro O. TAKAHASHIB
Takumi SANNOB
Yutaka AKAGIC
Hidemaro SUWAC
Satoshi FUJIMOTOBD
Masafumi UDAGAWAAA
Department of Physics, Gakushuin University, Mejiro, Tokyo, 171-8588, JapanB
Department of Materials Engineering Science, Osaka University, Toyonaka 560-8531, JapanC
Department of Physics, The University of Tokyo, Hongo, Tokyo, 113-0033, Japan
D
Center for Quantum Information and Quantum Biology, Osaka University,
Toyonaka 560-8531, Japan

In order to support and endorse the results
found in [1], we have reproduced the matrix
product renormalization group (MPRG) calculation done in [1] using the ISSP supercomputer. We newly find that by using the ISSP
supercomputer the calculation with a larger
bond dimension χ is possible. Indeed, the calculations with χ = 128 are possible for bothtransverse-field Ising model and the spin-1/2
antiferromagnetic Heisenberg model.

References[1] M. G. Yamada, T. Sanno, M. O. Takahashi, Y. Akagi, H. Suwa, S. Fujimoto,
M. Udagawa: arXiv:2212.13267 (2022).

The results agree well with an exact solution
or a quantum Monte Carlo simulation. Thismeans that MPRG has a potential to realizea large scale simulation up to χ = 128 anda good scalability. The advantage over othermethods will be discussed in the future, but it
is likely that MPRG is superior to other conventional tensor network methods.
Currently, the algorithm of MPRG is notsuitable for parallel computing, while it may
be straightforward to support a graphics
processing unit (GPU). We will persue thisdirection in the future study.

246

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Magnetism and superconductivity in ternary
chromium chalcogenides
Harald JESCHKE
Research Institute for Interdisciplinary Science, Okayama University
3-1-1 Tsushima-naka, Kita-ku, Okayama 700-8530

We study CrGeTe3 which is a van-der-Waals
bound layered ferromagnet. Materials like
CrGeTe3 or CrI3 have been intensely investigated in recent years because detached monolayers still order ferromagnetically and are discussed for various nanotechnological applications.
Recently, pressure experiments for CrGeTe3
reported an interesting temperature-pressure
phase diagram; importantly, at high pressures,
an insulator to metal transition roughly coincides with increases of Curie-Weiss and magnetic ordering temperatures. Our aim is to understand the phase diagram and to investigate
the nature of the strongly correlated metal athigh pressures.
We perform calculations using density functional theory (DFT) in combination with dynamical mean field theory (DFMT), as implemented in DCore. Based on experimental crystal structures, we prepare tight binding representations of the DFT bands for pressures
up to P = 10 GPa. Figure 1 shows the spectral function of a ferromagnetic insulating solution at ambient pressure [1]. The difficultyin this material is the fact that Te 6p bands
are very important at the Fermi level. The
insulator to metal transition involves pressure
induced widening of Te 6p bands which thenestablish a Fermi surface at around 5 GPa. By
analyzing the renormalization factor Z whereZ −1 = 1 − ∂ImΣ(iω)/∂ω|ω→0+ , we find that
correlations of some Cr 3d orbitals are enhanced by increasing pressure.

Figure 1: Spectral function for the ferromagnetic state of CrGeTe3 at P = 0 GPa andT = 100 K. Chromium magnetic moments areclose to 3 µB .

References[1] H.-X. Xu, M. Shimizu, J. Otsuki, H. O.
Jeschke, in preparation.

247

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Fluctuation exchange approximation calculations forthe superconducting transition temperatures andpairing symmetries of organic charge transfer salts
Harald JESCHKE
Research Institute for Interdisciplinary Science, Okayama University
3-1-1 Tsushima-naka, Kita-ku, Okayama 700-8530

BEDT-TTF (bisethylene-dithio-tetrathiafulvalene) is a famous nearly flat moleculethat forms hundreds of charge transfer (CT)salts, among them a number of famous superconductors. We study eight of the known
ambient pressure superconductors in the κphase where dimers of BEDT-TTF molecules
form a triangular lattice which is doped with
one hole per dimer. Our aim is to improveon earlier studies based on random phaseapproximation and two-particle selfconsistent approximation to predict the material
dependence of superconducting transition
temperature and order parameter. Our newly
developed multi-orbital, multi-site fluctuationexchange approximation (flex) code wasparallelized with OpenMP and MPI methods.

Figure 1 shows the leading solutions of theEliashberg equation for one of the materials,
together with the trend of the leading eigenvalues [1]. Tc trends are captured well by thedominant eigenvalue of the Eliashberg equation for five high-Tc materials. However, forthree low-Tc materials, the present approachoverestimates the strength of the s-wave solution. A possible reason for this discrepancy is
the intra-dimer, inter-site interaction V thathas been neglected so far and that is expected
to play an important role in the κ-type CT
salts.

Figure 1: (a), (b) Imaginary part of aninter-dimer element of the anomalous selfenergy for the two leading solutions forκ- (ET)2 Cu[N(CN)2 ]Br. (c) Eigenvalues λ foranisotropic s-wave and for s± + dx2 −y2 -wave.

References[1] M. Shimizu, D. Guterding, J. Otsuki, H.
O. Jeschke, in preparation.

248

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Numerical studies for science of bulk-edge correspondence
andtopological phases
Yasuhiro HATSUGAI
Department of Physics, University of Tsukuba
Tennodai 1-1-1, Tsukuba, Ibaraki 305-8571
The topological phases are independent ofover the torus spanned by the time and gauge

the symmetry breaking. It implies that there is

twist. In Fig. 1, we have shown numericalno fundamental order parameter to characterize

evaluation of the Chern numbers for theit. Most of them are gapped, and low-energy

topological pump of SU(Q) interacting fermions

gapless excitation is absent. It implies that the[2]. The relation between the edge statesbulk is hidden. However, with edges orspectrumboundaries, there exist low-energy gaplessconsistently discussed by using numericalexcitations that are localized only near thestudies.

and

the

Chern

numbersaresystem boundaries and characterize the phase.
This is the bulk-edge correspondence.
A topological pump (adiabatic pump) is
another kind of topological phenomenon where
the bulk-edge correspondence again governs thephenomena. Here the bulk-edge correspondenceis special, that is, the edge states are hidden sinceFig. 1 Numerical results of the Chern number ofthe gapless nature of the edge states implies that

SU(Q) topological pump (from ref. [2]).

the edge state cannot be observed by an

References

experimental finite speed pump. Experimental[1] Y. Hatsugai, Phys. Rev. Lett. 71, 3697

observables for the topological pump are the(1993).).

center of mass (polarization) of the bulk, which[2] Y. Hatsugai, and Y. Kuno, arXiv:2210.11646

is described by the Chern number integrated

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Eﬀects of ﬂux structures on Majorna excitations in
Kitaev spin liquids
Akihisa Koga
Department of Physics, Tokyo Institute of Technology, Meguro, Tokyo 152-8551

Quantum spin liquids in the Kitaev model
have been studied intensively in recent years.
It is known that in the Kitaev model, spin
degrees of freedom are split into itinerant
Majorana fermions and localized ﬂuxes dueto the spin fractionalization. The groundstate belongs to the ﬂux-free state and low
energy properties are described by itinerant
Majorana fermions. In fact, the Majoranaedge current has been observed in the halfquantized plateau in the thermal quantum Halleﬀect. Furthermore, the Majorana-mediatedspin transport has been theoretically suggested, which should stimulate further investigation to realize quantum devices using Majorana fermions.
(a)

𝒂!

𝒂"

𝒃!

𝒃"

be described by the toric code. Then a question arises. Are the above gapful states with
distinct origins are adiabatically connected toeach other? To answer this question, we consider several ﬂux conﬁgurations to investigate
how the anisotropy in the exchange couplings
aﬀects the Majorana excitations.
Motivated by this, we deal with theanisotropic Kitaev model with ﬂux conﬁgurations. In particular, we focus on the triangular
ﬂux conﬁgurations, where the ﬂuxes are periodically arranged in the honeycomb sheet, asshown in Fig. 1. Systematic numerical calculations demonstrate how the anisotropy of theexchange couplings and ﬂux conﬁguration induce the excitation gap. The induced gapped
quantum spin liquid states are distinct fromthe gapped one realized in the large anisotropic
limit. We then clarify the nature of gappedstates in the Kitaev model, regarding the ﬂux
conﬁguration as the superlattice potential forthe ﬂux-free state [2, 3].

References
(b)
1

(c)
Figure 1: Flux conﬁguration(q = 2) of theK'
Kitaev model.
X

ky/π

0.5

0

-0.5

-1

0.05 ΔY

In our previous
study [1], we have foundKΓthat the gapped quantum spin liquid is realized in a certain ﬂux conﬁguration. This
indicates that the ﬂux conﬁguration is a keyrole incontrolling Majorana excitation. On
kx/π
the other hand, it is known that the large
anisotropy in the exchange couplings stabilizes
the gapped quantum spin liquid, which should

-1

-0.5

0

0.5

[1] A. Koga, Y. Murakami, and J. Nasu,
Phys. Rev. B 103, 214421 (2021).

0.1

1[2] A. Hashimoto, Y. Murakami, and A.
0
Koga, J. Phys.: Conf. Ser. 2164, 012028(2022).
[3] A. Hashimoto, Y. Murakami, and A.
Koga, arXiv:2303.04295.

250

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Molecular dynamics simulation of disease-related
biomolecules
Hisashi Okumura
Exploratory Research Center on Life and Living Systems,
Institute for Molecular Science, Okazaki, Aichi 444-8585
Proteins maintain their functions with

the Institute for Solid State Physics, thecorrectly folded structures. However, when

University

of

Tokyo

[1].

The

replica-their concentration increases due to, forpermutation method is an improved version of

example, aging, they aggregate to form

the replica-exchange method, in which theoligomers, spherical aggregates, and amyloidtransition ratio of replicas in the temperature or

fibrils, needle-like aggregates. These protein

parameter space is improved.

aggregates are associated with about 40 humanAs a result, we identified the key residue,

neurodegenerative diseases. For instance, theArg5, for the Aβ42 dimerization. The two C-

amyloid-β

toterminal residues in Aβ42 tend to form contact

Alzheimer's disease. We have performed

with Arg5 because of the electrostatic attraction

several molecular dynamics (MD) simulations

between them, stabilizing the β-hairpin. This β-

of Aβ aggregates. In this fiscal year, we

hairpin promotes dimerization through theperformed MD simulations of the dimerizationintermolecular β-sheets.

(Aβ)

peptideisrelatedprocess of Aβ peptides and specified the key

Thus, we asked experimental collaborators

residue for aggregation of Aβ peptides.

toexaminetheeffectsofamino-acid

Aβ has two isoforms: Aβ40 and Aβ42.

substitutions of Arg5 to verify our theoretical

Although the difference between Aβ40 andprediction. We found that the mutations

Aβ42 is only two residues in the C-terminus,

remarkably suppressed the aggregation of Aβ42.

Aβ42 aggregates much faster than Aβ40. It is

In this way, our prediction was approved by the

not clear why the C-terminal two residues

experiments.

accelerate the aggregation. To investigate theperformed

References

molecular[1] S. G. Itoh, M. Yagi-Utsumi, K. Kato, and H.

dynamics simulations of two Aβ40 peptides andOkumura, ACS Chem. Neurosci. 13, 3139

those of two Aβ42 peptides using the(2022).

dimerization
Hamiltonian

process,

wereplica-permutation

supercomputer at the Supercomputer Center,251

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Nontrivial properties of interface and ﬁnite size
eﬀects of free energies
Naoko NAKAGAWA
Department of Physics, Ibaraki University
2-1-1 Bunkyo, Mito, Ibaraki 310-8512

We investigate liquid-gas coexistence associated with ﬁrst-order transitions via molecular dynamics simulations using LAMMPS. We
managed two kinds of systems, one is a simple
ﬂuid of Lennard-Jones particles and the otheris the two components ﬂuid of argon and krypton.
In the former systems, we notice the conﬁguration of the coexistence states and examine the thermodynamic quantities. Despite theﬁniteness of the system size, we ﬁnd a sharp
interface separating the two phases, whereas
both the shape and the position of the interfaces largely ﬂuctuate.
We set temperature and mean mass density
as almost particles are in liquid, while a small
number of particles be in gas. When the aspect ratio of the box conﬁning the particles isclose to unity, we observe typically a bubble
with a round-shaped interface. Changing the
aspect ratio, the shape of the interface transitioned into a ﬂat shape, where the gas occupies
a stripe region that may situate adjacent to theboundary. To characterize the transition, we
calculate the virial pressure corresponding to
the mean pressure over the system, which may
provide the global thermodynamic properties
in phase coexistence. With the transition forthe shape of the interface, we ﬁnd a significant change in the value of the virial pressure. It drops even to a negative value when
the size of the bubble is too small. This indicates that the system behaves attractively, although the liquid or gas without coexistence,

Figure 1: Phase coexistence of liquid and gas
in the aspect ratio 1 and 4.

or the coexistence with a ﬂat interface, shows
positive pressure as is usual. Such anomalyof the virial pressure suggests that the system
is not explained by thermodynamics in equilibrium, and may possess meta-stable or unstablecharacteristics. We will proceed calculations tocharacterize the local states for identifying themechanism to maintain the metastability.
We also investigated the liquid-gas coexistence for the two components ﬂuid of argon
and krypton using the mixing free energy. The
mixing free energy is numerically determinedby applying the formula derived in [1]. We
ﬁnd that the ﬁnite size eﬀects lead to the violation of the convexity in the free energy. To
examine the stability of the coexistence states
showing the violation of the convexity, we carefully examine the possibility of hysteresis. Up
to now, we conﬁrm that rather smaller systems
do not show hysteresis in our setup. While weneed further investigations with various systemsizes, the result indicates that the ﬁniteness ofthe system size may induce the violation of the252Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

convexity in free energy in a diﬀerent context
from the metastability.
We have used the L4 CPU of System B with
4 nodes in 512 parallel computation using MPI
for the calculation of 2 × 104 particles, the L16
CPU of System B with 16 nodes in 2048 parallel computation using MPI for the calculationof 8 × 104 particles, the L32 CPU of System
B with 24 nodes in 3072 parallel computation
using MPI for the calculations of 5 × 105 and1 × 106 particles.

References[1] A. Yoshida and N. Nakagawa: Phys. Rev.
Res. 4, 023119 (2022)

253

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Nonequilibrium relaxation temperature scaling:
Applications to quantum systems
Yoshihiko NONOMURA
MANA, National Institute for Materials Science
Namiki 1-1, Tsukuba, Ibaraki 305-0044

χst (t, δ)(δ − δc )γ ∼ fsc [ctσ + ln(δ − δc )γ ]. (1)Scaling results for the L×L periodic-boundary
system with L = 160 and inverse temperature
β = L in the loop algorithm from dimer conﬁgurations are given in Figure 1. Using theestimate γ = 1.400(3) obtained from this ﬁgure and the estimate γ/ν = 1.973(4) based on101χst(t,δ)(δ-δc)γWe numerically revealed that the critical
nonequilibrium relaxation behaviors in cluster algorithms are described by the stretchedexponential simulation-time dependence ofphysical quantities in various classical spin systems [1, 2, 3] and in a quantum phase transition [4], and derived such a relaxation formula
phenomenologically [5]. The nonequilibriumto-equilibrium (NE-EQ) scaling used in thesestudies [1, 3, 4] were generalized to oﬀ-criticalbehaviors, and such a scaling was called as the“temperature scaling” [6].
Here we show that the temperature scaling also holds in quantum phase transitions.
As an example, we consider the Néel-dimer
quantum phase transition in the columnardimerized S = 1/2 antiferromagnetic Heisenberg model on a square lattice [4]. Whenthe ratio of the strength of dimerized bonds
to other bonds are taken as (1 + δ) : 1,
the staggered susceptibility in equilibrium diverges in the vicinity of the critical point δc
as χst (t = ∞, δ) ∼ (δ − δc )−γ with δc =0.90947(6) [4]. Combining this formula with
the initial-time stretched-exponential criticalrelaxation form χst (t; δc ) ∼ exp(ctσ ), we haveχst (t, δ)(δ − δc )γ ∼ exp[ctσ + ln(δ − δc )γ ], orδ = 0.927100

δ = 0.928
δ = 0.930
δ = 0.932

10-1

δ = 0.935

L = 160
γ = 1.400(3)
10-2

-4

-2

0

2

4

δ = 0.940
δ = 0.945
6

8

10

12

cχtσ+ ln(δ-δc)γFigure 1: Temperature scaling of the staggered
susceptibility in the 2D columnar-dimerized
S = 1/2 antiferromagnetic Heisenberg model.
the NE-EQ scaling up to L = 160 [4], we have
ν = 0.710(2) only with a diagonal quantity χst .

References[1] Y. Nonomura, J. Phys. Soc. Jpn. 83,
113001 (2014).
[2] Y. Nonomura and Y. Tomita, Phys. Rev.
E 92, 062121 (2015).
[3] Y. Nonomura and Y. Tomita, Phys. Rev.
E 93, 012101 (2016).
[4] Y. Nonomura and Y. Tomita, Phys. Rev.
E 101, 032105 (2020).
[5] Y. Tomita and Y. Nonomura, Phys. Rev.
E 98, 052110 (2018).
[6] Y. Nonomura and Y. Tomita, Phys. Rev.
E 102, 052118 (2020).

254

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

HPC-based fusion of quantum simulation, experiment
analysis and data-driven science
Takeo Hoshi1,21Department of Mechanical and Physical Engineering, Tottori University,
4-101 Koyama-Minami, Tottori 680-8550, Japan.

2

Slow Positron Facility, Institute of Materials Structure Science, High Energy Accelerator
Research Organization (KEK), Oho 1-1, Tsukuba, Ibaraki, 305-0801, Japan.
We developed several new features on

surface structure as a test of the Bayesian

2DMAT [1-6], an open-source data-analysis

inference by PAMC. [8] We analyzed thesoftwareexperiment diffraction data in the one-beamforadvanced

experimental
wascondition, in which only the 𝑧 -coordinate ofdeveloped by the PASUMS project at FY2020,

atom positions are measured. The Bayesian2021[1]. A major achievement in the present

posterior probability distribution 𝑃(𝑋|𝐷) was

project is the automated optimization of thecalculated for the data set of the atom positions

massivelythe𝑋 with the given experimental diffraction data

population annealing Monte Carlo (PAMC)

set 𝐷 and reproduced the correct atomic

method [7]. As results, we found an almost

position of the 𝑁" = 6 atoms of the top-mostideal parallel efficiency not only on the ISSP

and several subsurface layers. Moreover, thesupercomputer (ohtaka) but also on the Fugaku

correct number of the atoms (𝑁" = 6) was alsosupercomputer with upto 10! nodes [8] . As an

reproduced from the calculated model evidence

application study, we used 2DMAT for the data

𝑃(𝐷). In conclusion, the PAMC analysis, as aanalysis of total-reflection high-energy positron

global search, determined the correct surface

diffraction (TRHEPD)[9]. TRHEPD is an

structure without any initial guess. Other

innovative measurement technology for theapplication studies are ongoing.

measurement

techniques.

parallel

2DMAT

computationofsurface structure analysis and was realized at
Slow

Positron

Facility,

KEK

[9].

ReferencesThe

experimental setup of TRHEPD is similar to

[1]

that

https://www.pasums.issp.utokyo.ac.jp/2DMATofreflection

high-energy

electrondiffraction (RHEED) but TRHEPD reveals the[2] K. Tanaka, et al. (T. Hoshi), Acta. Phys. Pol.

surface sensitivity and was used for the

A 137, 188 (2020).

determination of surface structure.

[3] T. Hoshi, et al. (T. Hoshi), Comp. Phys.

2DMAT

was applied to the analysis of Ge(001)-c4x2

Commun. 271, 108186 (2022).

255

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo[4] T. Hanada, et al., Comp. Phys. Commun.

[7] K. Hukushima, Y. Iba, AIP Conf. Proc. 690,

277 108371 (2022).

1604 (2003).

[5] Y. Motoyama, et al.

[8] N. Kinoshita, et al. (T. Hoshi), in JPS(T. Hoshi), Comp.

Phys. Commun. 280, 108465 (2022).

meeting, 22-25, Mar. 2023.

[6] K. Tanaka, et al. (T. Hoshi), JJAP Conf.

[9] https://www2.kek.jp/imss/spf/eng/

Series 9, 011301 (2023).

256

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Chaotic dynamics in quantum many-body systems
Masaki TEZUKA
Department of Physics, Kyoto University, Kitashirakawa, Sakyo-ku, Kyoto 606-8502
The Sachdev-Ye-Kitaev (SYK) model is amodel of fermions with independently random
all-to-all four-point interactions obeying theGaussian distribution. The model, both for
Majorana and complex fermions, has ananalytic solution in the limit of large number of
fermions. The Lyapunov exponent, defined by
the out-of-time ordered correlator, realizes theuniversal upper bound at low temperatures.
Previously, we studied the effect of arandom hopping term, two-fermion interactions,
added to the model [1]. The chaos boundbehavior is lost and the eigenenergy spectrum
becomes uncorrelated. The localizationtransition can be understood as a Fock-space
localization, a type of many-body localization.
We have obtained physical quantities related tothis transition, such as the location of thetransition point as well as the moments of theeigenstate
wavefunctions
andbipartiteentanglement entropy close to the transition
analytically and confirmed numerically [2].
A “sparse” version of the SYK model, in
which the number of randomly chosen non-zero
interaction terms is only on the order of thenumber of fermions, have been proposed a few
years ago, and for Gaussian random couplings,
the sparse model reproduces essential featuresof the original SYK model [3]. Using the ISSP
Supercomputer, we analyzed the spectral
statistics of a further simplification of thesparse SYK model, in which the magnitude of
the nonzero couplings between Majorana
fermions are set to be a constant [4]. In fact,
this simplification is an improvement, as the
spectral correlation is stronger and thus closer
to the random-matrix universality for the same

number of non-zero couplings, compared to theGaussian random case.
We also studied the quantum error
correction (QEC) capabilities of the unitary
time evolution according to the SYK type
Hamiltonians by the Hayden-Preskill protocol.
This is a process in which one tries to decode
an unknown quantum state thrown into aquantum many-body system with theknowledge of the initial state of the system andthe time evolution, by accessing only a part ofthe system after the evolution. The error
estimate for random unitary evolutions obeying
the circular unitary (Haar) ensemble has been
known. For the sparse SYK model, the QEC
error estimate approaches the Haar value after ashort time if the spectral statistics is randommatrix like. In the presence of two-fermion
interactions, departure from the Haar value isobserved before the Fock space localization [5].

References[1] A. M. Garcia-Garcia, B. Loureiro, A.
Romero-Bermudez, and M. Tezuka, Phys. Rev.
Lett. 120 (2018), 241603.
[2] F. Monteiro, T. Micklitz, M. Tezuka, and A.
Altland, Phys. Rev. Research 3 (2021), 013023;
F. Monteiro, M. Tezuka, D. A. Huse, and T.
Micklitz, Phys. Rev. Lett. 127 (2021), 030601.
[3] S. Su et al., arXiv:2008.02303; A. M.
Garcia-Garcia et al., Phys. Rev. D 103, 106002(2021).
[4] M. Tezuka, O. Oktay, E. Rinaldi, M.
Hanada, and F. Nori, Phys. Rev. B 107,
L081103 (2023).
[5] Y. Nakata and M. Tezuka, arXiv:
2303.02010.

257

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Molecular Dynamics Simulation of Dissolution of Stretched PEG
Crystal in Aqueous Solution
Koichi Mayumi, Kosuke Aomura, Haru Shinohara

The Institute for Solid State Physics,
The University of Tokyo, Kashiwa-no-ha, Kashiwa, Chiba 277-8581
Hydrogel is a material in which

consisting of 24 PEG chains with 42

water is confined in a polymer network.

repeat units were created (Fig. 1). After

Hydrogel has high biocompatibility andenergy minimization calculations, 4200

is expected to be applied to biomaterials,

water molecules are added to the box so

but its mechanical strength has been athat the polymer fraction was 38 wt%. A

problem. In our laboratory, we havestretching force was applied to bothdeveloped

both

ends of each polymer chain for 400 ns.

toughness and resilience by using the

The applied force f was set at 6 points: 0,

self-reinforcing effect [1]. In this self-

10, 30, 70, 100, and 210 kJ/(mol nm).

reinforcing

GROMACSahydrogel

gel,

with

polyethylene

glycol2016.5

was

used

as

(PEG) is used as the main chain of thecomputational software. Simulations

polymer network and water as theweresolvent. When the strain is removed, thesupercomputer system B. The number

PEG chains form hydration with theof atoms in theperformedon

Ohtaka,ISSP

solvent water, and the crystals arequickly melted. Such solvation anddissolution processes are thought toplay an important role in the straininduced crystals that are reversible
against elongation. In this study, we
performed all-atom molecular dynamics
simulations to understand extended
PEG

crystaldissolvesinaqueoussolution.

Fig. 1 Snap-shots of PEG zig-zag crystals
stretched under f = 10 kJ/(mol nm) at time t= 0 and t = 400 ns.

Based on the structures reported from
X-ray diffraction results [2, 3], all-atommodels of zigzag PEG chain crystals

258

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

simulation box was about 24,000, andchain crystal is dissolved and then the athe computation speed was about 200

helical PEG crystal is formed..

ns/day with a parallel number of 512.

References[1] C. Liu, N. Morimoto, L. Jiang, S.
Kawahara, T. Noritomi, H. Yokoyama,
K. Mayumi, K. Ito, Science, 372, 10781081(2021).
[2] Takahashi, Y., Tadokoro, H.
Macromolecules, 6, 672-675 (1973).
[3] Takahashi, Y., Sumita, I., Tadokoro,
H. J. Polym. Sci., B, Polym. Phys., 11,2113-2122 (1973).

For f = 0 kJ/(mol nm), the zigzag
chain crystals are dissolved within 200
ns, which is consistent with the rapid
dissolution of stretched induced PEG
crystals just after the applied stretching
force is unloaded in our experiments.
When f is 10 kJ/(mol nm), the zigzag

259

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Molecular dynamics simulation for chemical reactions in
multicomponent systems of thermosetting resins
Yutaka Oya
Department of Materials Science and Technology,
Tokyo University of Science, Niijuku, Katsushika-ku, Tokyo 125-8585
In this year, we have studied following two

simulations were performed for reproducing the

topics. The first is to establish the methodology

equilibrium structure of the system including IT-

for reproducing the crosslinked reactions of

4F (donor molecule) and PDTB (accepter

thermosetting polymers based on full-atomistic

molecule) as shown in Figure 1. Further,molecular dynamics simulation. We focused on

frequency distributions for each type of stacking

multicomponent system including base resin

structure in the system were analyzed. As awith epoxy groups, curing agent with amine

result, it was found that J-aggregation between

groups and lignin with hydroxyl groups, which

IT4F and PDTB molecules is dominant in theis one of promising candidates for matrix of bio-

system. In the future study,

based composites. The chemical reactions
among these components were successfully
realized for systems of several hundred thousand
atoms by LAMMPS software.

In the future

study, we systematically investigate the thermal
and mechanical characteristics, such as Young’s
modulus, strength, glass-transition temperature,
for the crosslinked structures formed by these

Figure 1. (a) Equilibrium structure of IT4F andmolecules.

PDTB molecules. (b) Snapshots of typicalThe second is to investigate the stacking

stacking structures obtained by molecular

structure of organic photovoltaic materials. It is

dynamics simulations.

well known that electron transfer occurs throughthe π-π stacking structure of the donor andacceptor molecules. Such photoexcited reactionsof conjugated polymers are expected to be
appliedtonondestructive

inspectionofmechanical damages of a composite. In thisstudy,

full

atomicmolecular

dynamics

260

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Numerical study on low-energy states of quantum
spin systems
Hiroki NAKANO
Graduate School of Science, University of Hyogo
Kouto, Kamigori, Hyogo 678-1297, Japan

Since researchers are often faced with
many-body problems in the condensed matter physics, it is diﬃcult to estimate physicalquantities precisely. One of the typical issues isa quantum spin system. To study a quantum
spin system, numerical approaches have widelyand eﬀectively been employed. Computational
studies that were carried out have provided us
with useful information of the target systems.
Within the field of quantum spin systems,
three methods have been eﬀectively used. The
first and traditional one is the numerical diagonalization method. The second method is
the quantum Monte Carlo (QMC) simulation.
The third one is the density matrix renormalization group (DMRG) method. Each of thesemethods has advantages; however, it has also
disadvantages at the same time. The QMC
simulations can treat significantly large systems irrespective of the spatial dimensions of
the systems; on the other hand, the negative
sign problem with this approach prevents us
with a diﬃculty in precise evaluation of physical quantities in frustrated systems. When thespatial dimension of a target system is one,however, the DMRG method is very useful irrespective of whether or not the target system includes frustration. Unfortunately, thismethod is still under development for the cases
when the spatial dimension is larger than one.
We can apply the numerical diagonalization
method to various systems irrespective of thepresence of frustrations and the spatial dimension. This method, on the other hand, hasa severe weak point that it can treat only
systems with very small sizes. To overcome
this disadvantage of the numerical diagonalization method, we developed a hybrid-typeparallelized code of Lanczos diagonalization[1].
This code enables us to treat various large systems that have not been treated yet within thismethod. We, therefore, study various quantum spin systems by this method as a primary
approach in this project.
In the project in 2022, we tackled theS = 1 Heisenberg antiferromagnet in one
dimension[2]. We successfully carried out
Lanczos diagonalizations of this system up to
30 sites under the twisted boundary condition. The 30-site system was treated within
Lanczos-diagonalization studies for the firsttime to the best of our knowledge. Note herethat the largest treated matrix dimension is18,252,025,766,941. Our results were analyzed
via a convergence-acceleration technique; wefinaly concluded∆/J = 0.410479239 ± 0.000000041
as the Haldane gap, namely, the spin excitation
above the nondegenerate ground state. We
confirmed that this result agrees with the result from the DMRG method[3]. We also studied the S = 1/2 Heisenberg antiferromagnet on
the floret-pentagonal lattice[4]. Our calculations successfully clarified the behavior aroundthe five-ninth of the saturation magnetizationin its magnetization process. Our studies contribute much to our deeper understandings of261

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

the quantum magnetism. Further investigations would clarify nontrivial quantum eﬀects
in these systems.

References[1] H.
NakanoandA.
Terai:
J. Phys. Soc. Jpn. 78, 014003 (2009).
[2] H. Nakano, H. Tadano, N. Todoroki and
T. Sakai: J. Phys. Soc. Jpn. 91, 074701(2022).
[3] H. Ueda and K. Kusakabe: Phys. Rev. B
84, 054446 (2011).
[4] . R. Furuchi, H. Nakano, and T. Sakai:arXiv:2209.13887 to be published in JPS
Conf. Proc.

262

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Study of ground states and dynamics in frustrated
quantum magnets using MPS-based simulations
Matthias GOHLKE
Theory of Quantum Matter Unit, Okinawa Institute of Science and Technology
1919-1 Tancha, Onna-son, Okinawa 904-0495

Competing interactions—or frustration—
between the constituents of a many-body system have proven to be an origin of exotic
phases of matter. A famous example in thiscontext are quantum spin liquids (QSL), that
do not exhibit conventional magnetic orderof spin-dipolar moments, but rather are defined by emergent gauge fields, fractionalized
excitations, and topological order. A topicalexample in this context are Kitaev-like frustrated magnets, that exhibit a bond-dependent
spin-exchange resulting in exchange frustration and, for certain choices of parameters,
can result in a QSL ground state. Part ofthe computational resources provided by ISSP
has been used to study the related anisotropic
Kitaev-Γ model [see (II)].

cally been observed in frustrated ferromagnets
with ring-exchange on the square lattice [2].
Experimentally, the nature of such a groundstate is intrinsically difficult to verify, due tothe lack of probes that couple directly to thespin-quadrupole moments. Instead, it is necessary to examine the dynamics of a QSN: A
continuous symmetry for the director of a spinquadrupole remains, that give rise to a gapless
Goldstone mode [3, 4].

On the other hand, ordering of higher moments like spin-quadrupoles may occur as well.
Unlike spin-dipolar order, spin-quadrupolarorder is defined by a director—an arrow without a head—in close analogy to the rods ofnematic order and liquid crystals. Thus, spinquadrupolar order is also named quantum spin
nematic (QSN). Part of the resources havebeen used to study the QSN phase stabilizing in a frustrated ferromagnet with external
magnetic field [see (I)].

Here, we study the square-lattice frustrated
J1 -K model [1], with dominant ferromagnetic Heisenberg exchange J1 between nearest
neighbor spins, cyclic ring-exchange K aroundsquares, and hz the Zeeman coupling to a magnetic field along the z-axis. Using iDMRG andthe matrix product states (MPS) framework as
well as a recently developed exact diagonalization (ED) method near saturation [5], we confirm the existence of the QSN phase in an extended range of K/|J1 |. Overall we find a good
agreement between high-field ED on symmetric clusters and iDMRG on cylindrical geometries with a circumference of Lcirc = 6 sites.
The QSN phase is sandwiched between highfield polarized and 4-sublattice AF phase. Additional phases (Neél, vector-chiral) occur forlarge K.

(I) QSN in a Spin- 12 Frustrated Ferromagnet. Here, we focus on a quantum magnet
with spin- 12 degrees of freedom. Such magnets
can only exhibit a QSN states if two spin- 12
are combined into an effective spin-1 [1]. In
fact, such QSN states on bonds has theoreti-

Given the ground state as an MPS, we
can then obtain dynamical properties via atime-evolution unitary U (dt) represented asa matrix product operator. We confirm thecondensation of two-magnon bound state at
the corresponding wave vector of the QSN

263

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Figure 1: TM spectrum illustrating dominantcorrelations at kx = 0 as well as incommensurate correlations near kx ≈ 0.35π.
state. Within the QSN phase, we observe (1)the magnon-like excitation that remains fully
gapped, and (2) a gapless mode with its spectral weight in the dipolar structure factor vanishes as q → 0 & ω → 0 as predicted by priormean-field studies. We do, however, observe
relevant qualitative differences: an additionalmode with a gapless node at an incommensurate, field-strength dependent wave vector
occurs. Within this term, we have focusedon identifying the incommensurate mode bymeans of the MPS transfer matrix (TM). The
TM spectrum contains all the information on
correlations. Momentum information is contained through the complex phase of the TM
eigenvalues, and momentum along the circumference being a well defined quantum number
as long as translation symmetry remains intact. Thus, the position in momentum spaceof the gapless modes can be extracted from the
MPS, cf. Fig. 1.. Indeed, we find perfect agreement between the gapless mode in the dynamics and the TM, which will help us in further
elucidating the nature of the incommensurate
gapless mode accompanying the QSN phase.
All MPS-related simulations have been carried out using TenPy [8]. TenPy makes useof Cython and calls heavily optimized Lapack
and Blas routines matrix operations, e.g. singular value decomposition.
(II) Extended QSL in an Anisotropic
Kitaev-Γ Model. The characterization ofquantum spin liquid phases in Kitaev materials has been a subject of intensive studies over
the recent years. Most theoretical studies havefocused on isotropic coupling strength in an attempt to simplify the problem. We, instead,
have focused on an extended spin-1/2 KitaevΓ model on a honeycomb lattice with an additional tuning parameter that controls the coupling strength on one of the bonds [6], allowing us to connect the limit of isolated Kitaev-Γ
chains, which is known to exhibit an emergent
SU (2)1 Tomonaga-Luttinger liquid (TLL) [7],to the two-dimensional model. We find thatthe critical properties of the TLL persist for
finite inter-chain coupling, and a QSL develops in analogy to sliding Luttinger liquids thatdiffers from the Kitaev spin liquid.
We have employed the TenPy package [8] toobtain the ground state in MPS form, study its
scaling behavior via the MPS transfer matrix
spectrum, and obtain dynamical properties.

References[1] A. Andreev and I. Grishchuk, Sov. Phys.
JETP 60 (1984) 267.
[2] N. Shannon, T. Momoi, and P. Sindzingre, Phys. Rev. Lett. 96 (2006) 027213.
[3] R. Shindou, S. Yunoki, and T. Momoi,
Phys. Rev. B 87, (2013) 054429.
[4] A. Smerald, H. T. Ueda, and N. Shannon,
Phys. Rev. B 91 (2015) 174402.
[5] H. Ueda, S. Yunoki, and T. Shimokawa,Comp. Phys. Comm. 277 (2022) 108369.
[6] M. Gohlke, J. C. Pelayo, T. Suzuki,
arXiv:2212.11000 (2022). See also report
by T. Suzuki.
[7] Yang et al. Phys. Rev. Lett. 124, 147205(2020).
[8] J. Hauschild, and F. Pollmann, SciPost
Phys. Lect. Notes 5 (2018).

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Dynamical properties of the extended Kitaev-G model on ahoneycomb lattice
Takafumi SUZUKI
Graduate School of Engineering,
University of Hyogo, Shosha, Himeji, Hyogo 670-2280
dispersion curves for the low-lying excitation
show a linear behavior around gapless wavenumber points, which is similar to thecharacteristics observed in the S=1/2
antiferromagnetic Heisenberg chain. In thisproject, we have investigated the remaining
part; the ground state and the excitations for K≦0 and G≧0. We have found that in the spin
chain limit, an incommensurate phases exists
between the TLL phase and D4-symmetrybreaking phase [5]. In addition, we have foundthat the presence of the extended Kitaev spin
liquid [5] is still controversial [7]. This implies
that the extended Kitaev spin liquid phase isreplaced by a different state from the D3ordered state [5]. These states we newly foundin the chain limit changes two-dimensional
long-range orders when the interchaininteraction becomes finite [7].

We have used ISSP supercomputer resources
for the following two topics: (1) dynamical
properties of the extended honeycomb-lattice
Kitaev-G model and (2) supersolid states in
two-dimensional hard-core bosonic Hubbard
model with dipole interactions.
(1) Dynamical properties of the extended
honeycomb-lattice Kitaev-G model
In this project, we have investigated groundstates and dynamical properties of the S=1/2
Kitaev-G model [1] on a honeycomb lattice by
the numerical exact diagonalization anddensity-matrix-renormalization-group
approaches. The S=1/2 Kitaev-G model on ahoneycomb lattice was proposed as theeffective model of the honeycomb-latticemagnet, a-RuCl3. So far, the ground-statephase diagram of this model with the isotropic
coupling strength has been studied intensively.
In recent experiments on the related compounds,
RuX3 (X=Br, I) [3], it has been reported that
applying pressure can control the anisotropy ofthe coupling strength. The ground-state phase
diagram of the S=1/2 Kitaev-G model withanisotropic interactions has not been fully
understood yet. Note that the anisotropy meansthat by tuning the anisotropy, the modelchanges from the Kitaev-G spin chains to theisolated dimer model via the isotropicallyinteracting model.
In the previous study, we focused on the
ground phase diagram and the low-lying
excitation in the case where the Kitaev
interaction (K) and the G interaction (G) arenegative and positive, respectively [4]. We
clarified that the Tomonaga-Luttinger liquid(TLL) appearing in the spin chain limit (d=0)[5] persist for the finite interchain interactions
and it becomes a proximate TomonagaLuttinger liquid (pTTL), which is analogous tosliding TLL [6]. In the pTTL phase, the(2) Supersolid states in two-dimensional hardcore bosonic Hubbard model with dipole
interactions
In this project, we have studied the phase
diagram of two-dimensional hard-core bosonic
Hubbard model with dipole interactions. Manyefforts have been made to realize supersolidstates so far and ultracold-atomic gases trappedin optical lattices are candidate stages to study
the supersolid states. In lattice Bose-Hubbard
models, frustrated interactions play a key roleto stabilize the supersolid states. In ref. [8],authors investigated the phase diagram of thetwo-dimensional bosonic hardcore Hubbard
model with the dipole interactions by the meanfiled approach and the infinite entangled-pairstate. It was found that several supersolid states
appear in between solid phases with different
commensurate fillings by tilting the angle ofthe dipole axis from the direction perpendicular
to the lattice plane. However, the range of thedipole interactions is restricted within a short265

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

range in ref. [8]. Thus, when the farther
neighbor interactions are included, whether
those supersolid phases are stable or not isunclear.
We have investigated the phase diagram of thetwo-dimensional bosonic hardcore Hubbard
model with the dipole interactions by quantum
Monte Carlo computations and have applied
two methods to discuss the details of eachphase; finite-size-scaling analysis and machinelearning assisted approach. First, we havestudied the phase diagram of the same model
considered in ref. [8]. We have obtained
consistent results with those by the iPEPS
calculations. Next, we have investigated thephase diagram by changing the cut-off range ofthe dipole interactions. It has been found thattwo quarter solid states (r=1/4 and 3/4, where r
is filling.) becomes unstable and diagonal-stripe
solid states (r=1/3 and 2/3) becomes stable.
The supersolid state related with the quarter
solid disappears.

References[1] J. G. Rau, E. Lee, and H. -Y. Kee, Phys. Rev.
Lett. 112, 077204 (2014).
[2] T. Yamada, T. Suzuki, and S. Suga, Phys.
Rev. B 102, 024415 (2020).
[3] Y. Imai, et al., Phys. Rev. B 105, L041112(2022) and Y. Choi, et al., Phys. Rev. B 106,174430 (2022).
[4] M. Gohlke, J. C. Pelayo, and T. Suzuki,
arXiv:2212.11000.
[5] W. Yang, et al., Phys. Rev. Lett. 124,
147205 (2020) and W. Yang, A. Nocera, and I.
Affleck, Phys. Rev. Research 2, 033268 (2020).
[6] R. Mukhopadhyay, C. L. Kane, and T. C.
Lubensky, Phys. Rev. B 64. 045120 (2001).
[7] M. Gohlke, J. C. Pelayo, and T. Suzuki, in
preparation.
[8] H.-K. Wu and W.-L. Tu, Phys. Rev. A 102,053306 (2020).

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Systematic Searches for Grain Boundary Structures
and Structure-based Prediction of Multiple
Properties
Susumu FUJIIDivision of Materials and Manufacturing Science, Osaka University
Yamadaoka, Suita, Osaka, 565-0871
Nanostructures Research Laboratory, Japan Fine Ceramics Center
Mutsuno, Atsuta, Nagoya 456-8587

Grain boundaries (GBs) are ubiquitus in inorganic solid materials except for glasses. At
GBs, electronic and atomic structures thatare diﬀerent from its corresponding crystal
structure are formed, providing unique properties that often determine macroscopic physical responces of materials. The GB structures
vary signiﬁcantly depending on the orientation between adjoined crystal grains, resulting in a variety of GB properties. Revealing
GB structure-property relationship is important to improve the performance of structuraland functional materials based on material microstructures.
To correlate GB structures with material
properties, there are two technological diﬃculties. One is that a large-scale data for GB
structures and their properties, with an accuracy comparable to ﬁrst principles calculations, is necessary to provide a general andsystematic understanding that is applicable toexperimental design of microstructures. Another is that the computational and machinelearning method to quantify GB structures andcorrelate them with properties is not well established. Regarding the latter, the authors
reported that the use of structure descriptor
and hierarchical clustering can clasify the local atomic environments composing GB structures, and multiple linear regression with theclassiﬁed data provide an accurate predictionof GB thermal conductivities for MgO withphysical interpretations [1].
The main drawback of our previous work isthat we use an emprical potential to derive
MgO GB structures and their thermal conductivities. In this study, we applied an accurate machine learing potential for silicon, distributed in MACHINE LEARNING POTENTIAL REPOSITORY [2], to our GB structure searches and property evaluations, to give
more precise GB structure-property relationship [3]. Silicon is known as a typical semiconductor, and considered as a promising candidate for thermoelectric material. To reduce its
high thermal conductivity, nanocrystalline silicon with high GB population has been extensively synthesized, leading to very low thermal
conductivity less than 5 W/mK [4]. Revealing
the impact of Si GBs on thermal conduction
as well as electronic conduction is necessary
for further improvement of its thermoelectric
conversion eﬃciency.
We mainly performed GB structure searcheswith the methods of rigid body translations (RBTs) and structure optimization, andthermal conductivity calculations using perturbed molecular dynamics (MD). The necessary codes for RBTs and perturbed molecular
dynamics were developed by the author, andthe latter was implemented to the Large-scale
Atomic/Molecular Massively Parallel Simula-267

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyotor (LAMMPS) [5].
Using the code for RBTs and LAMMPS forstructure optimization, we derived 45 silicon
GB structures. Then, perturbed MD simulations were performed for calculating thermal
conductivity of each GB structure. The perturbed MD simulations are highly computationally demanding, requiring large computational cells containing from three to thirteen
thousands of atoms and with the time stepsof more than one million. Repeating this simulation ﬁve times with diﬀerent magnitude of
perturbations for each GB yielded a set of statistically accurate thermal conductivities. As
230 separate perturbed MD simulations werenecessary, all of them were performed in a single node rather than multiple nodes.
Compiling the GB structure data and thermal conductivity shows that bond angle strains
at GBs signiﬁcantly decreases their thermal
conductivities. In addition, structural voids,
formed at a few GBs, largely decreases GB
thermal conductivities. These trends resultsfrom the features of covalent silicon, where one
atom bonds strongly with four atoms to forma tetrahedron, and the strong directionality ofthe bonds tends to create voids at GBs.

Figure 1: An example of silicon grain boundary structure derived using a machine learningpotential.
[3] B. Jugdersuren et al., Phys. Rev. B. 96(2017) 014206.
[4] S. Fujii and A. Seko, Comput. Mater. Sci.
204 (2022) 111137.
[5] S. Plimpton, J. Comput. Phys. 117 (1995)
1.
[6] J. Hickman and Y. Mishin, Phys. Rev.
Mater. 4 (2020) 033405.

In contrast to our results, a revious reportusing an empirical force ﬁeld claims that GB
energy is the dominant factor for determining GB thermal conductivity of silicon, rather
than structural features of GBs [6]. A large
scale calculations using supercomputers andthe accurate machine learning potential indicate the importance of precisely estimating GB
structures and their properties to reveal a robust GB structure-property relationship.

References[1] S. Fujii et al., Nature Commun. 11 (2020)
1854.
[2] A. Seko, arXiv:2007.14206.

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Kinetics of phase transition and polyamorphism
Kazuhiro Fuchizaki, Katsumi Nakamura, and Hiroki Naruta
Department of Physics, Ehime University, Matsuyama 790-8577

In FY2022, we continued to undertake thetwo tasks dealt with in FY2021: One was addressing the validity of the two-phase thermodynamic (2PT) model put forth by Goddard
et al. [1], and the other was a new trial application of machine learning (ML) to judge theorder of a phase transition.
One of our major issues is to explain a signof the Clapeyron slope of a liquid-liquid transition (LLT) boundary. Liquid phosphorus [2]and sulfur [3] are known to undergo an LLT.
However, the former (latter) LLT boundary
has a negative (positive) slope, although both
liquids are believed to be brought to highdensity liquids via polymerization. Because a
specific volume must be reduced upon an LLT,
the entropy diﬀerence between high- and lowdensity liquids can determine the slope’s sign.
Therefore, a method to precisely evaluate a liquid entropy is required. The 2PT model has
been widely used for this purpose. However,
because the model was based on many assumptions, we must examine the validity before applying the model.
The second task is a brand-new research
project that has not been tried thus far. We
could finish the project in FY2022, and thesubmitted paper is now under review. Most of
the computation time was exhausted in preparing the learning data.

where D and DHS are the diﬀusion coeﬃcients for the system in question and the corresponding hard-sphere fluid, respectively. ρ
and T are the system’s density and temperature. We explicitly include the dependenceof DHS on the hard-sphere diameter σ. The
neat trick of Goddard et al. in deriving aclosed equation to determine f was to replace
the RHS of Eq. (1) with D(ρ, T )/D0HS (T, ρ; σ),thereby replacing the definition of f as f =D(ρ, T )/D0HS (T, ρ; σ). Enskog’s theory thenallows us to relate DHS to the diﬀusion coeﬃcient D0HS and the compressibility factor
obtainable from the Carnahan–Starling equation of state. Furthermore, D0HS (T, f ρ) =D0HS (T, ρ)/f is valid in the zero-pressure limit
at which the Chapman–Enskog result is valid.
These relations regarding the diﬀusion coeﬃcients resulted in the cubic equation for f .

The self-diﬀusion coeﬃcient of a hard-spherefluid
Our detailed examination ascertained that the
2PT model bears its validity in estimating theliquid fraction f , which is implicitly defined by

On our way to evaluating the RHS of Eq. (1),
we noticed that the empirical formula proposedby Speedy [5] could reproduce the simulated
DHS quite satisfactorily. This confirmation enabled us to interpolate the RHS with other f s
without carrying out simulations. Finally, we
numerically solved Eq. (1), whose RHS was

f = D(T, ρ)/DHS (T, f ρ; σ),(1)Here, we directly solved Eq. (1) instead ofsolving the cubic equation. We employed themodified Lennard-Jones (mLJ) system with
2048 particles for this judgment. We first evaluated the diﬀusion coeﬃcient D, Eq. (1) numerator, for the mLJ system at the thermodynamic conditions reported in [4]. Next, we conducted molecular dynamics simulations for a
hard-sphere fluid to find DHS . We set ρ = 0.05,
0.331, and 0.6 and σ = 1.0, 1.1, 1.2, and 1.3,
where ρ and σ are presented in the mLJ units.

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

thus evaluated, to obtain the desired f , which
was found to be relatively close to that obtainable from the cubic equation. Therefore, regarding estimating the molecular fraction, we
concluded that the treatment of Goddard etal. was adequate.
Judging the (dis)continuity of phase transition
using ML [6]Our short-term memory often causes “memory hysteresis” against back-and-forth input
of continuously varying input. For example,
imagine a series composed of gradually varying
pictures from A to B. Suppose that an A-likepicture certainly changes to a B-like when we
see picture C in the series when we see thosepictures from A to B. When we see them in
the reverse order, a picture in which we recognize a definite change from B-like to A-like will
diﬀer from C, thus inducing memory hysteresis. The size of hysteresis may increase withthe smoothness of variation. Because an artificial neural network (ANN) is so constructedthat it can reproduce the functionality of a biological neural network (BNN), an ANN would
behave like a BNN when a set of continuously
changing patterns is input in back-and-forth
directions. We then expected that an ANN
could distinguish a continuous or discontinuous phase transition from its hysteresis behavior when it sequentially learns a series of phasetransition patterns generated as a function of
external parameters such as temperature or external field.
We implemented a convolutional neural network (CNN) to realize an ANN. To test
the phase-transition order, we employed twodimensional Ising and q-state Potts models
with q = 3 through 6. For the Ising model,
we generated spin configurations below andabove the critical temperature Tc as a function of the external field h. For the Potts
model, we obtained them around the phasetransition point as a function of temperature
T . h or T constituted the labels. We let the
CNN learn the diﬀerence between the configu-

rations with adjacent labels in increasing (decreasing) order of the labels to assess the hysteresis size. The learning eﬀects were stored in
the CNN’s weights, and we monitored the sumof the weights, Wsum , connected to the output
layer. For the specifications of the CNN andthe models, see [6].
We have already reported the results of thepreliminary study [4]. For the Ising crossovertransition, hysteresis appeared in Wsum ’s behavior plotted as a label function, whereas nohysteresis was recognizable for the transition
below Tc as expected. However, unlike theIsing case exhibiting an order-to-order transition, the Potts model did not allow the CNN tolearn the configurations in reverse order, i.e.,from the disordered to the ordered state. The
CNN did not seem to capture the disorderedstate uniquely.
We could resolve this issue by “renormalizing” the q states into a single state to
make the CNN facilitate capturing the Potts(dis)ordering aspect [6]. We confirmed that
Wsum ’s hysteresis arose for q = 3 and 4,
whereas Wsum took maximum precisely at thetransition point without hysteresis regardlessof the learning direction. Interestingly, Wsum
behaved marginally for the weak first-ordertransition realized when q = 5.

References[1] S-T. Lin, M. Blanco, and W. A. Goddard:
J. Chem. Phys. 119 (2003) 11792.
[2] Y. Katayama et al.: Nature 403 (2000)
170.
[3] L. Henry et al.: Nature 584 (2020) 382.
[4] K. Fuchizaki et al.: ISSP Supercomputer
Center Activity Report (2021) 256.
[5] R. J. Speedy et al.: Mol. Phys. 66 (1989)
577.
[6] K. Nakamura and K. Fuchizaki: submitted to J. Phys. A.

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Numerical Study of Magnetism in the
Honeycomb-Lattice Spin Systems
Chitoshi YASUDA
Department of Physics and Earth Sciences, Faculty of Science,
University of the Ryukyus, Okinawa 903-0213, Japan
Many low-dimensional antiferromagnets
with excitation modes separated from theground state by a ﬁnite energy gap have beensynthesized, and eﬀects of impurities on thosematerials in the gapped state have attracted
our interest. In the previous work [1], thespin S = 1/2 and S = 1 two-dimensional
quantum Heisenberg antiferromagnets on asquare lattice, which is composed of bondalternating chains and interchain interactions, have been investigated by the quantum
Monte Carlo simulation with the continuousimaginary-time loop algorithm, and groundstate phase diagrams parametrized by thestrength of the bond alternation and that of
the interchain coupling have been determined
precisely. The phase diagrams reveal under what conditions spin-gapped states andmagnetically ordered states are realized. Recently, various honeycomb-lattice systems areinterested in relation to quantum spin liquidstates. For example, the Kitaev model on
the honeycomb lattice has the qunatum spin
liquid state as the ground state.

this technique, the S = 1 system is represented by an S = 1/2 system with special
boundary conditions in the imaginary-time
directions. The phase-transition points of theground state are estimated by the ﬁnite-sizescaling analyses of the correlation lengths
evaluated by the second-moment method.
For valid analyses, it was necessary to calculate up to a system of size Lx × Ly =96 × 48, where the temperature is ﬁxed to
T = 1/Lx . The critical exponent for the correlation length ν is ﬁxed at 0.71 consistent
with the value for the three-dimensional classical Heisenberg model. In the S = 1/2 system, there are the dimerized state and the antiferromagnetic long-range ordered (AFLRO)
state as the ground state. On the other
hand, in the S = 1 system, there is the Haldane state in addition to the dimerized state
and the AFLRO state. The phase diagram
parametrized by the strength of the bond alternation and that of the interchain couplingshow that the region of the dimerized stateof the honeycomb-lattice system is narrower
than that of the square-lattice system.

In this project, we research the S = 1/2
and S = 1 quantum Heisenberg antiferromagnets on the anisotropic dimerized honeycomb lattice using the quantum Monte Carlosimulations with the continuous-imaginarytime loop algorithm [2], and compare the results with those of the square lattice. The
honeycomb lattice is composed of bondalternating chains and interchain interactions. Periodic boundary conditions are imposed in the x and y directions. For the simulation of the S = 1 system, the subspinrepresentation technique is applied [2, 3]. In

References[1] M. Matsumoto, C. Yasuda, S. Todo, and
H. Takayama, Phys. Rev. B 65, 014407(2001).
[2] S. Todo and K. Kato, Phys. Rev. Lett.
87, 047203 (2001).
[3] K. Harada, M. Troyer, and N.
Kawashima, J. Phys. Soc. Jpn. 67,
1130 (1998).

271

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Spin Dynamics Simulation of the Z2-vortex Fluctuations
Hikaru KAWAMURA
Molecular Photoscience Research Center, Kobe University, Kobe 657-8501
Yo P. MIZUTA
Graduate School of Engineering Science, Osaka University, Toyonaka 560-8531
Kazushi AOYAMA
Graduate School of Science, Osaka University, Toyonaka, 560-0043
In as early as 1984, it was pointed out that theresolution by an order of magnitude.4) The

frustrated isotropic Heisenberg magnets in two

dynamical spin structure factor computed at T

dimensions (2D) could possess a novel vortex

slightly above TV is shown in the figure below,

characterized by the parity-like two-valued

which indeed exhibits a narrow central peak, in

topological number corresponding only to its

addition to the side peaks of spin-wave origin.

presence/absence, a Z2 vortex, which drove a

Quantitative analysis of the data indicates that

topological transition at a finite temperature TV

the width of the central peak indeed becomes asassociated with its binding-unbinding.1) Whilenarrow as ~0.001J in the close vicinity of the K-

the direct experimental observation of the Z2
vortex has remained elusive for years, a very
recent quasi-elastic neutron scattering (QENS)
experiment performed on the powder sample ofthe quasi-two-dimensional (2D) triangularlattice Heisenberg antiferromagnet NaCrO2 has
succeeded in directly probing the signature of
the free Z2 vortex via the observation of a sharp

point slightly above TV (=0.285J), consistently

quasi-elastic (QE) scattering of its energy widthwith the recent QENS experiment. 2)

as narrow as ∼10 μeV corresponding to ~0:001J

References(J the exchange coupling).2) The observed peak[1] H. Kawamura and S. Miyashita, J. Phys. Soc.

width was even narrower than that in the earlaierJpn. 53, 4138 (1984). [2] K. Tomiyasu et al, Phys.

model simulation3) by an order of magnitude.

Rev. B 106, 054407 (2022). [3] T. Okubo and H.

We then have performed further extensive spin-

Kawamura, J. Phys. Soc. Jpn. 79, 084706 (2010).

dynamics simulation on the frustrated 2D[4] Y.P. Mizuta et al, J. Phys. Soc. Jpn. 91,

Heisenberg model by improving the energy

035001 (2022).

272

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Finite temperature calculation of quantum manybody
system using
random phase product state and neural network
wavefunction
Toshiaki IITAKA
Center for Computational Science,
RIKEN, 2-1 Hirosawa, Wako, Saitama, 351-0198 JAPANCalculation of thermal average of an

Thermal average of observable A atobservable A of a closed quantum many-

inverse temperature β is calculated using

body system in its thermal equilibrium atrandom state Φ as

an inverse temperature is one of the most=A

important problems in computational
physics. It has, however, two major

tr[e − β H A]
=
Z

Φ AΦ
Z

where

computational difficulties. The number ofZ ( β )= tr[e− β H ]=quantum states to be included and theΦΦdimension of each quantum state vector

is the partition function and the double

are both exponentially increases as thebracket indicates statistical average overtemperature and the system size increases,

random states. The thermal state isrespectively. Each difficulty has been

defined by the imaginary time evolution

independently attacked using randomof the random state.

state method [1, 2] and variational

Φ(β ) = e− β H / 2 Φwave function, respectively.

Thermal energy E and specific heat

In this project, we introduced

C are expressed as

random state method to general form ofvariational wave functions. Only few

E (β ) =papers have investigated the applicationof random state method to variationalH

andwave functions [3-7].

2C (β ) kB β 2  E 2 − E  .
=



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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Variational wave function is a point
on a manifold embedded in the Hilbert
space,

ψ ( w) = ∑ c( w, σ) σσwhich is parameterized with variationalparameters, w = {wi } , whose number ismuch less than the dimension of theHilbert space.

Fig.1 : Specific heat per site for 14

Thermal state has been introduced insite spin-1/2 antiferromagnetic

some variational wave functions such as

Heisenberg chain with PBC

MPS and PEPS as

calculated with RBM( α = 4 ) and1024 samples.

Ψ ( β ; w) = e − β H /2 Ψ ( w)Referenceswhere Ψ ( w) is random variational[1]

wavefunction.

T. Iitaka, and T. Ebisuzaki, Phys.

Rev. Lett. 90, 047203 (2003).

In this project, the above[2]

formulation was extended to general form

T. Iitaka, and T. Ebisuzaki, Phys.

Rev. E 69, 057701 (2004).

of variational wavefunction by[3]

K. Takai et al., J Phys Soc Jpn 85,

calculating the imaginary time evolution

034601 (2016).

with stochastic reconfiguration[3, 8]. It is[4]

noted that the trace is formally taken over

S. Garnerone, T. R. de Oliveira, andP. Zanardi, Phys. Rev. A 81, 032336 (2010).

the entire Hilbert space in our method[5]

T. Iitaka, arXiv:2006.14459 (2020).

while it was limited to its subspace in the[6]

A. Iwaki, A. Shimizu, and C. Hotta,

previous method[3]. A numerical

Physical Review Research 3, L022015 (2021).

example for Restricted Boltzmann[7]

Machine is computed with NetKet [9]S. Goto, R. Kaneko, and I. Danshita,

Phys. Rev. B 104, 045133 (2021).

and the result is shown in Fig. 1.

[8]

S. Sorella, Phys. Rev. Lett. 80, 4558

(1998).
[9]

G. Carleo et al., SoftwareX 10,100311 (2019).

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Tensor data analysis by tensor network
Kenji Harada
Graduate School of Informatics, Kyoto University
Kyoto 606-8501, Japan

Recently, data in various ﬁelds have been
stored as a multi-dimensional array called atensor. When the number of attributes (indexes) of a tensor increases, the size of thetensor becomes huge because it increases exponentially as the number of indexes increases.
Therefore, we need the high-speed and highquality use of tensor data of huge sizes.
A tensor network (TN) is an eﬀective tool for
representing a huge composite tensor and has
been extensively developed in quantum information and statistical physics research. Since
the tensor network can eﬀectively represent
such high-dimensional data, we may eﬃciently
process the general tensor data.
Previous studies [1, 2] proposed using ofa tree tensor network (TTN) for a multidimensional probability distribution. In thisproject, we try to improve the performance ofa TTN algorithm for generative modeling tomulti-dimensional data distribution.
Here, we consider a generative model basedon a quantum state [1, 2]. Following the Bornrule, we deﬁne p(x) as the square of the amplitude of a wave function:p(x) =|ψ(x)|2,Z(1)

no loop structure. Thus, an MPS is a specialized type of TTN. As shown in [2], the performance depends much on the network topology
of TTNs.
We propose a new algorithm to change
the network structure of a TTN for generative modeling. Several strategies can beused to select a better choice of local network
structure[3]. Our network optimization improves the performance of generative modelingwith a TTN.

References[1] Zhao-Yu Han, Jun Wang, Heng Fan, Lei
Wang, and Pan Zhang. Unsupervised Generative Modeling Using Matrix ProductStates. Phys. Rev. X, 8(3):031012, 2018.
[2] Song Cheng, Lei Wang, T. Xiang, and Pan
Zhang. Tree tensor networks for generativemodeling. Phys. Rev. B, 99(15):155131,
2019.
[3] Kenji Harada, Tsuyoshi Okubo, and Naoki
Kawashima. Network optimization of tree
generative models. in preparation.

where ψ(x) is a wave function and Z is thenormalization factor.
MPSs [1] and balance TTNs [2] have been
proposed to deﬁne the wave function for generative modeling. The number of indexes of atensor in the balance TTN is equal to that in
the MPS. The only diﬀerence is the topology ofthe network; all physical indexes in the TTN,
xi , are connected, and the TTN network has

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Molecular dynamics simulation of BaTiO3 nanostructure II
T. Hashimoto
National Institute of Advanced Industrial Science and Technology (AIST),
Tsukuba Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, JapanThe ferroelectric domain wall (FDW) has dielectric, electrical, and mechanical properties
that diﬀer from those of bulk. It can evenbe considered a two-dimensional phase diﬀerent from the bulk. In our previous study, we
found that 71◦ and 109◦ Ising-like FDWs areobserved in BaTiO3 nanoclusters[1]. In thisstudy, we focused on the FDW structures andenergies of rhombohedral BaTiO3 .
We used a shell model proposed by Tinte etal.[2]. The calculations were performed withthe code developed by us. We performed calculations for 71◦ , 109◦ , and 180◦ {110} FDWs in
the rhombohedral phase. We used 10×6×6 5atom unit cells for the 109◦ FDWs, and 8×6×6
and 10×6×6 10-atom building blocks for the71◦ and 180◦ {110} FDWs, respectively. The
initial Ising FDW conﬁgurations were made byshifting the Ti atoms in one direction in halfof the supercell, and in the other direction inanother half. The initial Bloch FDW conﬁgurations were made in the same way as the Isingcase except that the local dielectric polarization vectors were rotated across the FDW. To
mimic the isolated FDW between very thickdomains in the supercell calculations, the inplane lattice constants were set to be the sameas those of the single domain phase, while theout-of-plane lattice constant was relaxed.
Thedomainwidthsandenergies had been calculated with the
Ginzburg–Landau–Devonshire (GLD) modeland the GLD model with reduced gradientterms (GLD-r) [3]. The GLD and the GLD-r

results predict that the 109◦ Bloch wall ismore stable than the 109◦ Ising wall energy,
which is not in accordance with the LDA
results. The GLD-r model predicts narrower
walls and lower wall energies compared to the
GLD model, which improves the energy andwidth of the 71◦ Ising wall and the width of
the 109◦ Ising wall, but overshoots the energyof the 109◦ Ising wall and the energy andwidth of the 109◦ Bloch wall. The GLD-r
model predicts that the 180◦ Bloch wall isunstable compared to the 180◦ Ising wall,
which is not in accordance with the LDA
results.
The shell model results also have inconsistencies compared to the LDA results: The
FDW energy of the 109◦ Ising wall is too low.
The FDW energy of the 180◦ {110} Bloch wall
is too high and the width of it is too narrow.
However, it predicted that the 71◦ Ising wall
has the lowest energy, followed by the 109◦
Ising wall and the 109◦ Bloch wall.

References[1] T. Hashimoto and H. Moriwake, Physica
B, 656, 414768 (2023).
[2] S. Tinte, M. Stachiotti, S. Phillpot,
M. Sepliarsky, D. Wolf, and R. Migoni: J.
Phys.: Condens. Matter 16 (2004) 3495.
[3] M. Taherinejad, D. Vanderbilt, P. Marton, V. Stepkova, and J. Hlinka: Phys.
Rev. B 86 (2012) 155138.

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Numerical study of quantum many-body scars inbosonic systems
Ryui KANEKO
Department of Physics, Kindai University
Higashi-Osaka, Osaka 577-8502, Japan

Ĥ = Ĥ0 + Ĥint ,X †Ĥ0 = −J(âi âi+1 + â†i+1 âi ),

(1)
(2)

i

Ĥint =UXn̂i (n̂i − 1).
2

6
S vN/ ln 2Understanding how isolated quantum manybody systems thermalize is of great interestin physics. The eigenstate thermalization hypothesis (ETH) provides a sufficient condition
for thermalization and is satisfied in many
quantum systems in the presence of interactions [1, 2]. However, recent experimentsof nonintegrable systems suggest ETH breaking. This phenomenon originates from the special eigenstates exhibiting extremely slow thermalization [3, 4]. Such quantum many-bodyscar (QMBS) states exist in various spin models [5–10]. It is desirable to search for the
QMBS states in other quantum systems, which
can be realized in experiments.
Here we focus on the Bose-Hubbard modelsuitable for experiments using ultracold atoms
on optical lattices. The Hamiltonian is given
as

N =8
Scars

4

U/J = 1

2
0
−10

−5

0

5
Energy/J

10

15

Figure 1: Entanglement entropy as a functionof energy. The QMBS states are circled. We
consider the system size L = 8 and the interaction strength U/J = 1 in Eq. (1). The
quantum number sector with the particle number N = L and the even parity I = +1 isgiven by blue dots. Each QMBS state (a red
circle with a dot) with the particle number
N has the energy U N/2 and the expectation
PPL
2
values ⟨ L
i=1 n̂i ⟩ = N and ⟨ i=1 n̂i ⟩ = 2N .
Its bipartite entanglement entropy at N = L,corresponding to a state |Sn=L/2 ⟩, becomesvN → [ln(πL/8) + 1]/2 for L → ∞.
SA

(3)

i

We specifically choose the model in a truncatedHilbert space with the maximum occupation
number nmax = 2. Note that three-body lossesof atoms on optical lattices realize this constraint [11, 12].
We numerically diagonalize the Hamiltonianand find the eigenstates with a small entanglement entropy (see Fig. 1). We also analytically show that they are generated by applying the SU(2) ladder operator consisting ofa linear combination of two-particle annihilation operators. The construction of the QMBS
states is equivalent to the case of the S = 1 XY
model [13, 14].
These findings will stimulate further research on the QMBS states in general BoseHubbard models without any constraints,
which would be more suitable for experimentsof ultracold atoms in optical lattices. The construction of the QMBS states in general spin

277

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

S systems has been discussed recently [15, 16],
which could be extended to the Bose-Hubbard
model by utilizing the improved HolsteinPrimakoff transformation [17, 18].

References[16] L.-H. Tang et al., Phys. Rev. Research 4,043006 (2022).
[17] M. Vogl et al., Phys. Rev. Research 2,043243 (2020).
[18] J. König and A. Hucht, SciPost Phys. 10,
7 (2021).

[1] W. Beugeling et al., Phys. Rev. E 89,
042112 (2014).
[2] H. Kim et al., Phys. Rev. E 90, 052105(2014).
[3] H. Bernien et al., Nature 551, 579 (2017).
[4] D. Bluvstein et al., Science 371, 1355
(2021).
[5] C. J. Turner et al., Nat. Phys. 14, 745(2018).
[6] C. J. Turner et al., Phys. Rev. B 98,
155134 (2018).
[7] A. J. A. James et al., Rev. Lett. 122,
130603 (2019).
[8] N. Shibata et al., Phys. Rev. Lett. 124,
180604 (2020).
[9] M. Serbyn et al., Nat. Phys. 17, 675(2021).
[10] S. Moudgalya et al., Rep. Prog. Phys. 85,
086501 (2022).
[11] A. J. Daley et al., Phys. Rev. Lett. 102,040402 (2009).
[12] M. J. Mark et al., Phys. Rev. Lett. 108,215302 (2012).
[13] M. Schecter and T. Iadecola, Phys. Rev.
Lett. 123, 147201 (2019).
[14] S. Chattopadhyay et al., Phys. Rev. B
101, 174308 (2020).
[15] N. O’Dea et al., Phys. Rev. Research 2,043305 (2020).

278

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Tensor-network study of the quench dynamics of theKitaev honeycomb model
Ryui KANEKO
Department of Physics, Kindai University
Higashi-Osaka, Osaka 577-8502, Japan⟨i,j⟩γ ,γ∈{x,y,z}energy density

-0.06

D=2
-0.07 D=3
D=4
-0.08 D=5
-0.09 D=6
D=7
-0.1 D=8
-0.11
-0.12
-0.13
0.1

1
time

10

Figure 1: Time evolution of the energy density.
absolute value of z-component magnetization

The Kitaev honeycomb model [1] in themagnetic field displays a rich phase diagramof quantum phases [2, 3]. These findings stimulate further research on the nonequilibrium
dynamics of the model. One of the motivations for studying dynamics is an experimental
interest; the Kitaev candidate materials may
exhibit unconventional transport [4–6]. The
other is an interest in statistical mechanics as
to how the integrable Kitaev model, which israre in quantum 2D systems, thermalizes in thepresence of additional interaction [7, 8]. These
interests motivate us to study the dynamics
of the simple Kitaev model with the magnetic
field. As a benchmark, we specifically focus
on the sudden quench of the ferromagnetic Kitaev model starting from the antiferromagnetic
state without the magnetic field. Previous investigations are limited to mean-field calculations or numerical-diagonalization studies forsmall systems. We use the 2D tensor-network
method using the infinite projected entangledstate (iPEPS) [9, 10], which can directly deal
with inifinite systems accurately, to examine
how far one can go for the quench dynamics ofthe Kitaev model.
We prepare the staggered antiferromagnetic
state, whose spins are oriented along the zaxis, on a honeycomb lattice as the initial state|ψ(0)⟩. The state can be prepared by the bonddimension D = 1, and is time evolved by theS = 1/2 Kitaev HamiltonianX(1)Ĥ = −J γ Ŝiγ Ŝjγ ,1
0.8
0.6 D=2
D=3
0.4 D=4
D=5
D=6
0.2 D=7
D=8
0
0.1

1
time

10

Figure 2: Time evolution of the absolute valueof the z-component magnetization.
where γ corresponds to the bond direction andJ γ is the Kitaev interaction. Here we consider the isotropic and ferromagnetic interaction J γ = 1. The wave function at eachtime |ψ(t)⟩ = exp(−itĤ)|ψ(0)⟩ is calculated by
the simple update algorithm [11, 12] using thetensor-network library TeNeS [13]. The wave
function is optimized up to the bond dimension
D = 8 to examine how physical quantities converge by improving the accuracy of the state279

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

with increasing the bond dimension.
The energy should be conserved for thequench dynamics. We can estimate to what extent the simulation goes well by looking at thedegree of conservation. In the present study,
the energy is nearly conserved for tJ γ ≲ 4when D ≳ 4 (see Fig. 1). Within this reliable
time frame, the magnetization nearly vanishes
at tJ γ ≈ 4. This observation is consistent withthe previous study, where an emergent nonmagnetic steady state has bee proposed [7].
Our study would serve as a reference for understanding the quench dynamics of the Kitaev model and relevant unconventional steadystates, which can be identified by utilizing the
2D tensor-network method. It is left for future
work to investigate the effect of the magnetic
field in the model.

[11] J. Jordan et al., Phys. Rev. Lett. 101,
250602 (2008).
[12] H. C. Jiang et al., Phys. Rev. Lett. 101,
090603 (2008).
[13] Y. Motoyama et al., Comp. Phys. Commun. 279, 108437 (2022).

References[1] A. Kitaev, Ann. Phys. (NY) 321, 2(2006).
[2] Z. Zhu et al., Phys. Rev. B 97, 241110(R)(2018).
[3] C. Hickey and S. Trebst, Nat. Commun.
10, 530 (2019).
[4] J. Nasu and Y. Motome, Phys. Rev. Research 1, 033007 (2019).
[5] T. Minakawa et al., Phys. Rev. Lett. 125,047204 (2020).
[6] M. Udagawa et al., Phys. Rev. Lett. 126,
127201 (2021).
[7] L. Rademaker, SciPost Phys. 7, 071(2019).
[8] G.-Y. Zhu and M. Heyl, Phys. Rev. Research 3, L032069 (2021).
[9] T. Nishino et al., Prog. Theo. Phys. 105,409 (2001).
[10] F. Verstraete and J. I. Cirac, arXiv:condmat/0407066.

280

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

The Study on Superionic Conduction and Phase Transition by Disordering ofComplex Hydride
Ryuhei Sato
Advanced Institute for Materials Research,
Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, JapanThe complex hydrides such as LiCB9H10

order

are promising candidates for electrolytes dueofmagnitudeataroundroom

temperature [2].

to their high ionic conductivity and stability
to

metal

anodes[1].

Althoughthedevelopment of such complex hydrides is
progressing

rapidly,themechanismofsuperionic conductivity induced by the phasetransition due to the disordering (≈ the change
in rotational motion) of the complex anions is
not clear. Therefore, the purpose of this study
is to clarify the effect of the disordering on
the ion conduction and the phase transition by

Figure 1 Arrhenius plot of Li self-diffusion

using ab initio molecular dynamics (AIMD)

coefficient during NNP-MD simulations ofand neural-net potential molecular dynamics

LiCB9H10 with and without CB9H10− rotation(NNP-MD) simulations.

[2].

AIMD simulation at 1000K confirmed

As shown in the discussion above, it isthat LiCB9H10 exhibits concerted motion, in

possible to quantitatively analyze the changes

which

migratein ion transport caused by anion-rotation

simultaneously with the rotation of theusing NNP-MD simulations. However, it iscomplex anion. To clarify the effect of the

difficult to obtain a specific relationship

CB9H10− rotation, we analyzed the change inbetween the anion rotation and Li migration.

diffusion coefficient with and without the

Therefore, in order to visualize the ion

rotation using NNP-MD simulations (Fig. 1).

transport mechanism in such a disordered

As shown in the figure, the activation energy

system, we have developed an ion-migration-

increases when the anion rotation is stopped,mechanism analysis method using topologicaland the conductivity decreases by nearly one

data analysis based on persistent homology.

multiple

Li

ions

281

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

This year, we investigated the applicability ofmolecules improves ionic conductivity is also

topological data analysis on ion migrationunclear. Therefore, we created a workflow tomechanism [3]. Here, we choose α-AgI as aanalyze the effect of such neutral moleculesbenchmark, because this system retains theon complex hydrides. Given a composition

ordered I bcc lattice while Ag ions areincluding neutral molecules, complex anions,disordered in the system and shows complex

and cations, a structure search method based

concerted ion migration. MD simulations at

on genetic algorithms (USPEX) can estimate

various temperatures confirmed specific ring

the stable and reasonable structure including(one-dimensional hole in terms of homology)

neutral molecules after several hundred DFT

structures appear only when the system shows

calculations. The activation energies of ion

high ionic conductivity. Inverse analysis of

migrationthese ring structures revealed that they were

simulations using the obtained structures are

four-membered rings consisting of two Ag

in good agreement with experimental values.

and I ions. By deforming these four-

The obtained trajectories of ion migration

membered rings along the probability density

during the metadynamics simulation confirm

distribution of the ring structure obtained bythat the coordination structure of the neutral

topological data analysis, we succeeded inmolecule

visualizing the representative concerted ion

significant effect on the activation energy. As

migration of Ag ions in α-AgI[3]. In thea result of machine learning using thefuture, we plan to apply this technique toestimatedcomplex hydride systems to clarify theconfirmed that the distance between anions

relationship between anion rotation and ion

and the electronegativity are important ones

migration.

for the activation energy [5].

estimated

duringion

activationfrommetadynamics

migrationenergies,

has

it

a

was

References

Recently, complex hydrides are expected
to be applied as multivalent-ion (ex. Zn2+ and[1] W. Tang et al., Adv. Eng. Mater., 6 (2016)Mg2+) conductors [4]. Although the insertion

1502237.

of neutral molecules into complex hydrides is[2] R. Sato et. al., to be submitted.

known to be effective to improve the ion[3] R. Sato et al., J. Chem. Phys., 158, (2023)

conductivity

144116.

for

these

multivalent-ion

systems, there is no clear guideline to choose[4] K. Kisu et al., J. Mater Chem. A, 10

most effective neutral molecules for these(2022) 24877.

complex hydrides. In addition, the specific[5] S. Egon, R. Sato et al. submitted to NPJ.

mechanism of how the insertion of neutral

Comput. Mater.

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Theoretical studies on dynamics in biomolecular and liquid
systems
Shinji Saito
Graduate University for Advanced Studies (Sokendai) / Institute for Molecular Science,
38 Nishigo-Naka, Myodaiji, Okazaki 444-8585, Japan
Fluctuations are inherent in condensed
phase

systems,such

asliquidattempt to understand the conformational

andorigins of such dynamical disorder.

biomolecular systems, and occur acrossdiversespatio-temporalscales.

In the second half of the 2022 fiscal year,

These

we constructed a Kv.12 model of about 140k

fluctuations play a significant role in structuralatomschanges and reaction dynamics, thereby

concentration of 0.15 M and performed

influencing various properties and functions.

roughly 100 μs molecular dynamics (MD)Consequently,ofsimulations at an external electric field of 500

fluctuations is crucial for a comprehensivemV using GROMACS. During the MD

understanding

relevant

simulation, about 2300 potassium permeation

chemical/biological processes. In the second

events were observed. The time series analysis

half of the fiscal year of 2022, our group

showed that these permeation events roughly

utilized the supercomputer resources at ISSP

followed the pattern of a Poisson process withfor mainly two related projects, 1) the iona slight deviation from it. Further analyses andpermeation process of potassium channel

MD simulations at different ion concentrations

Kv1.2, and 2) the excitation energy transfer ofand external electric field strength are to belight harvesting complex II (LHCII) in higher

performed for a complete picture of theplants.

dynamic disorder in the ion permeation

anofunderstanding
theKv1.2 is a mammalian voltage-controlled

at

the

electrophysiologicalprocess of Kv1.2.

potassium channel from the Shaker K+ channel

LHCII, where the first step of thefamily which regulates the cellular membrane

photosynthetic process occurs, has a complex

potential by conditionally releasing thestructure which makes its excitation energy

intracellular K+ ions. Since its crystal structuretransfer (EET) difficult to be elucidated

was resolved about twenty years ago, it has

experimentally. In order to understand the EET

become a typical and representative model for

dynamics of LHCII, we performed quantum

the study of ion channels. Here, we focus onmechanics (QM) calculation using the CAM-

the dynamical disorder revealed in the ion

B3LYP functional and determined the optimal

permeation processes under various ion

range separate parameters (between CAM andconcentrations and external voltages and

B3LYP) that reproduced the experimental
283

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

excitation energies of Chl a and b in threefunctional theory. The parallelized MMSIC-

solvents [1]. With these optimized parameters,

MD simulation sufficiently reproduced thewe refined the structures for 14 chlorophylls

QM/MM excitation energies on the fly and(Chl) in LHCII by using a modified version of

showed that the excitation energies of Chl a in

GAMESS on the supercomputer at ISSP. In

LHCII varies wider than in solvents.

order to study the dynamical properties, we
introduced

a

model

calledReferences

Molecular

Mechanics Shepard interpolation Correction[1] Zhu, Zhe, Masahiro Higashi, Shinji Saito,(MMSIC) that can give the approximate

Excited states of chlorophyll a and b inexcitation energies during MD simulation

solution by time-dependent density functional

based on the interpolation of optimized

theory, The Journal of Chemical Physics

structures

156(12) (2022) 124111.

using

the

above-mentioned

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Structural formation of inverse patchy particles
Takamichi TERAO
Department of Electrical, Electronic and Computer Engineering,
Gifu University, Yanagido 1-1, Gifu, 501-1132
Inverse patchy colloids are those in which theexhibit a variety of configurations by changing

anisotropy of interparticle interactions is due tothe ratio of positive and negative charges on theinhomogeneous surface charges [1]. Previous

surface.

studies of colloidal particle systems have largely

Figure 1 shows a snapshot of inverse patchy

dealt with the problem of aggregates, which arecolloids when the motion of each sphere wasdominated by attractive interparticle interactions,confined between a pair of two-dimensionaland colloidal crystals, which are dominated byplanes. From these results, we have revealed therepulsive interactions. Conventional patchy

dependence of the orientation order of theparticles are dominated by anisotropic attractive

colloids on the charge coverage ratio, as well asinteractions. Inverse patchy particles, on thethe phase diagram of the system.

other hand, consist of heterogeneously charged
units where the pair potentials between particles
contain both attractive and repulsive properties.
They are expected to exhibit novel properties
and their properties with respect to structureformation are of interest.
In this study, we focused on the structuralformation of inverse charged patchy particles.
The surface of these particles is charged and theFig. 1: Snapshot of inverse patchy colloids

sign of the charge is reversed between theunder geometrical confinement.

bipolar part and the other parts. The structureformation in these systems was studied bymolecular

dynamics

simulations.

ReferencesThe

simulations were performed with the LAMMPS[1] P. D. J. van Oostrum, M. Hejazifar, C.

simulation package in the NVT ensemble. In

Niedermayer, and E. Reimhult: J. Phys.:thermal equilibrium, inverse patchy particles

Condens. Matter 27 (2015) 2341.

285

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Effect of chain crossing prohibition on phase-separated
structure of block copolymers
Katsumi HAGITA,1 and Takahiro MURASHIMA2
1

Department of Applied Physics, National Defense Academy, Yokosuka 239-8686
2

Department of Physics, Tohoku University, Sendai 980-8578

We have studied phase-separated structures

To confirm fundamental behaviors usingof block copolymers in order to clarify

Gaussian chains, we performed large scale

topological effect of chain crossing prohibition.

DPD simulations to estimate lamellar domain

To achieve this aim, we firstly considered MP-

spacing of symmetric linear, ring, and four-

SRP extensions [1,2] of dissipative particle

arm-star block copolymer blends [7]. We also

dynamics (DPD) simulations [3]. We found that

confirmed practical compatibility of lamellara slight change in the potential (force field)

domain spacing between self-consistent field

between original DPD [3] and MP-SRP

theoryextensions [1,2] affects weakly the lamellar

fundamental understandings of topologicaldomain spacing. The numerical precision iseffects by chain crossing prohibition, we

related to potential validity problems, andperformed CGMD simulations of polymers

comparisonsphysics,

with rings. In [9], ring-filling effect on stress–

Gaussian and excluded volume chains, may notstrain curves of randomly end-linked tetra-arm

be appropriate. Thus, we switched our approach

prepolymers

to the development of force field parameters in

discovered topological transition in multicyclic

order to treat directly with the phase separation

chains with structural symmetry inducing

phenomenon in the coarse-grained molecularstress-overshootdynamics (CGMD) simulations of the Kremer-

multicyclic/linear

Grest

Althoughelongational flow. In addition, we investigatedimplementations of the MP-SRP extensions

topological barriers on the crystallization[1,2] were developed using LAMMPS [5], thebehavior of ring polyethylene melts with trefoil

CGMD approach has the advantage of beingknots via united atom MD simulations [11].

(KG)

betweendifferent

model

[4].

free from the implementation and able to use

andCurrently,

DPD

was

simulations

studied.

[8].

In

[10],

phenomena

weblends

we

in

under

investigatedForbiaxialinteraction

HOOMD-blue [6] on the GPU. Notably,parameters of AB di-block copolymers for

HOOMD-blue has a good performance for the

CGMD simulations. To consider a connection

standard DPD simulations [3].

to Flory-Huggins χ parameter, we are studying

286

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

relationships among interaction parameters,lamellar structure as shown in Fig. 1 (b). We

lamellar domain spacing, and chain length (N).

also confirmed that the lamellar domainFirst, we performed CGMD simulations of AB

spacing depends on the interaction parameters.

di-BCP starting from the lamellar structures asAftershown in Fig. 1 (a). We confirmed theparameters, we will investigate topologicalbehaviors of domain spacing from weak toconstrain effect of polymers such as rings via

strong segregation conditions were consistent

CGMD simulations.

furtherstudiesontheinteraction

with theories, simulations, and experiments.

References[1] R. D. Groot and P. B. Warren: J. Chem. Phys.
107 (1997) 4423.

[2] N. Iwaoka, K. Hagita and H. Takano: J. Chem.
Phys. 149 (2018)114901.
[3] K. Hagita, T. Murashima, H. Shiba, N. Iwaoka,
and T. Kawakatsu: Comput. Mater. Sci., 203(2022) 111104.
[4] K. Kremer and G. S. Grest: J. Chem. Phys. 92(1990) 5057.

[5] S. Plimpton: J. Comp. Phys. 117 (1995) 117.
[6] J. A. Anderson, J. Glaser, and S. C. Glotzer,
Comput. Mat. Sci. 173, 109363 (2020).
[7] K. Hagita, T. Murashima, and T. Kawakatsu,
Macromolecules 55, 8021 (2022).
[8] K. Hagita, and T. Murashima, Polymer 269,
125733 (2023).
[9] K. Hagita, T. Murashima, T. Ohkuma, andH. Jinnai, Macromolecules 55, 6547 (2022).
Fig. 1: Snapshots of CGMD models of AB di-[10] T. Murashima, K. Hagita, and T.

BCP forming lamellar structures.

Kawakatsu, Macromolecules 55, 9358 (2022).
[11] K. Hagita, T. Murashima, N. Sakata, K.

Second, we found that the AB-BCP in

Shimokawa, T. Deguchi, E. Uehara, and S.
Fujiwara, Macromolecules 56, 15 (2023).

CGMD simulations spontaneously forms a287

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Analysis of Various Crystal Forms of Glycine Nanocrystals
by Large-Scale Metadynamics Simulations
Hiroki NADA
Faculty of Engineering, Tottori University, 4-101 Koyama-Minami, Tottori 680-8552
The control of structure and shape offunctions represented by Gaussian window

glycine nanocrystals is important in connection

functions as collective variables indicated a few

with pharmaceutical engineering [1]. In thislocal

project, a classical molecular dynamics (MD)corresponded to a crystal-like structure with asimulation combined with the metadynamics

different molecular arrangement of glycine(MTD) method (hereafter, MTD simulation)

molecules.

was performed to elucidate stable structuresnanocrystal was also different between thoseand shapes of a glycine nanocrystal.

local minima. These results suggest that notminima.

Each

Moreover,

local

the

shape

minimumofthe

The MTD simulation was performed for a

only the structure of nanocrystals but also the

cubic system in which an α glycine nanocrystal

shape of them can be analyzed using the MTD

consisting of 64 glycine molecules was placed

method.

at its center. The remainder of the system was

In summary, the MTD method is a helpful

filled with water molecules. AMBER force

tool to search for stable structures and shapes offield

nanocrystals consisting of complex molecules.

was

used

for

estimation

of

the

intermolecular interaction energy and force
acting between glycine molecules. The MTD

References

simulation was carried out with DL_POLY 2.20

[1]

[2],

CrystEngComm. 17 (2015) 723.

in

which

PLUMED

1.3

[3]

was

X.

Yang

and

A.

S.

Myerson:implemented to permit combination with the[2] W. Smith and T. R. Forester: J. Mol. Graph.

MTD method, using System C of ISSP

14 (1996) 136.

Supercomputer Center.

[3] M. Bonomi et al.: Comput. Phys. Commun.
180 (2009) 1961.

A free energy surface (FES) obtained using
discrete Cα-Cα and N-N radial distribution

288

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Structural Analysis of Amorphous Silicon Monoxide by aMetadynamics Method with Unsupervised Machine Learning
Hiroki NADA
Faculty of Engineering, Tottori University, 4-101 Koyama-Minami, Tottori 680-8552
Amorphous SiO has been studied by many

Supercomputer Center. In this project, we

research groups because it can be functional

focused on the structural similarity between thematerials

molecular-scaleexamined amorphous phases. The structural

structure of it remains elusive. The purpose ofsimilarity was analyzed by evaluating thethis project was elucidating molecular-scale

geometrical similarity of Si-Si, Si-O, and Si-O

structure of amorphous SiO using a classicalpair-distribution functions. In this project, themolecular

simulationgeometrical similarity was evaluated using a

combined with the metadynamics (MTD)

method of unsupervised machine learning,

method (hereafter, MTD simulation [2]).

dimensional reduction technique [5].

[1].

However,dynamics(MD)

MTD is an enhanced sampling method that

We confirmed that the present methodology

increases the probability of reaching high free-

is helpful to investigate detailed structures ofenergy states by adding a Gaussian bias

multicomponent amorphous phases.

potential to the Hamiltonian of a state. The
MTD method affords a free energy surface

References(FES) in a space of collective variables (CVs)[1] A. Hirata et al.: Nat. Commun. 7 (2016)

for a system of interest. This FES quantitatively

11591.

represents[2] A. Laio and M. Parrinello: Proc. Natl. Acad.

therelative

thermodynamic

Sci. U. S. A. 99 (2002) 12562.

stabilities of many different states of the system.
The MTD simulation was carried out for[3] W. Smith and T. R. Forester: J. Mol. Graph.

each of amorphous Si, SiO and SiO2 phases

14 (1996) 136.

with DL_POLY 2.20 [3], in which PLUMED[4] M. Bonomi et al.: Comput. Phys. Commun.

1.3 [4] was implemented to permit combination

180 (2009) 1961.

with the MTD method, using System B of ISSP

[5] H. Nada: ACS Omega 3 (2018) 5789.

289

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Quantum algorithm for combinatorial optimization
Tatsuhiko SHIRAI
Department of Computer Science and Communications Engineering, Waseda University
Ookubo, Shinjuku-ku, Tokyo 169-8555⃗ ⃗γ ⟩ := e−iβp H e−iγp X · · · e−iβ1 H e−iγ1 X |+⟩ ,
|β,
(1)
⃗
where β = (β1 , . . . , βp ) and ⃗γ = (γ1 , . . . , γp ).
p denotes a circuit depth. X is called a∑mixer and adopted as X = i σix where ⃗σi =(σix , σiy , σiz ) is the Pauli operator acting onsite i. |+⟩ is deﬁned as σix |+⟩ = |+⟩ for all⃗ ⃗γ ) toi. The classical computer optimizes (β,⃗ ⃗γ | H |β,⃗ ⃗γ ⟩. We solve
minimize the value of ⟨β,
the quantum dynamics on the super computer
using openMP.
Maximum graph coloring problem colors
each node of a given graph G = (V, E) using ncolors so that the number of edges connecting
the nodes with diﬀerent colors is maximized.
This problem has a constraint that one color
is assigned to each node. When solving a COP
with constraints by QAOA, the Hamiltonian isgiven as H = Hobj + Hcst . Here, Hobj and Hcst

denote the Hamiltonians for objective function and constraints of the COP, respectively.
There are three methods to give the Hamiltonians: penalty method, domain-wall method,and spin-variable reduction method.
Figure 1 shows p dependences of successprobability ps . Here, we set n = 4 and |V | = 6,
and generate 10 random graphs with edge density 0.75. We found that domain-wall method
and spin-variable method outperforms penalty
method.
1

Success probability ps

We have developed a quantum algorithm for
QAOA (Quantum approximate optimization
algorithm), which solves combinatorial optimization problems (COPs) in gate-based quantum computers. This year, we proposed a new
method called spin-variable reduction method
for formulating a constrained COP as an Isingmodel [1] and compared the performance withconventional methods in maximum glaph coloring problems.
QAOA is a hybrid quantum-classical algorithm designed to approximate the groundstate of a Hamiltonian H. QAOA consists ofcalculations in a quantum computer and classical computer. The quantum computer calculates the expectation value of H over a variational state, which is given by

0.5

Penalty
Variable reduction
Domain wall
0

0

2

4

6

8

p

10

12

14

16

Figure 1: Performance comparison among
penalty method (squares), spin-variable reduction method (circles), and domain-wall method(triangles).

References[1] T. Shirai and N. Togawa: IEEE Transactions on Computers (Early Access).

290

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Topological chirality order and its stability inbreathing-kagome antiferromagnets
Kazushi AOYAMA
Department of Earth and Space Science, Graduate School of Science, Osaka University
Machikaneyama-cho, Toyonaka-shi, Osaka 560-0043

The quest for topological spin textures insolids is one of the central issues in condensed
matter physics because of their possible applications to spin-electronic devices. A typicalexample of such topological spin textures is
the so-called skyrmion crystal (SkX) where thetotal solid angle subtended by all the spin in
the magnetic unit cell takes an integer topological in units of 4π. Noting that the scalarspin chirality χijk = Si · (Sj × Sk ) is related tothe solid angle, the SkX could be regarded asa topological chiral order. Although the SkX
is usually realized in the presence of a magnetic ﬁeld being irrespective of whether thethe Dzaloshinskii-Moriya interaction is active
or not [1], a zero-ﬁeld SkX is also possible in a
speciﬁc metallic system [2]. Previously, we theoretically demonstrated that a breathing bondalternation of the underlying lattice can induce
a zero-ﬁeld topological chiral order which canbe viewed as a miniature version of the SkX[3]. In the work, we investigate eﬀects of an additional single-ion magnetic anisotropy whichmore or less exists in real materials.
We consider the following classical J1 -J3model on the breathing kagome lattice withthe single-ion magnetic anisotropy:
H = J1∑Si · Sj + J1′⟨i,j⟩S+J3∑⟨⟨i,j⟩⟩∑

Si · Sj⟨i,j⟩LSi · Sj + D∑(Siz )2 , (1)iwhere Si is a classical spin, the summation∑⟨⟩S(L) runs over site pairs on small (large)

triangles having the nearest neighbor (NN) interaction J1 (J1′ ), J3 > 0 is the third NN antiferromagnetic interaction along the bond direction, and D < 0 (D > 0) represents an easyaxis (easy-plane) anisotropy. In the isotropic
case of D = 0, a triple-Q state appearing fora relatively large J3 takes the miniature SkX
structure having a nonzero uniform total chirality [3]. By performing Monte Carlo simulations, we investigate the stability of this topological chiral order against the anisotropy D.
In our simulations, 1×106 - 2×106 MC sweeps
are carried out under the periodic boundary
condition, and the ﬁrst half is discarded forthermalization. Si at each site is updated ina conventional random and a successive overrelaxation-like processes, where in the former(latter) process, we try to rotate a spin ina randomly proposed direction (by the angle
π around the local mean ﬁeld) by using theMetropolis algorithm. Observations are doneat every MC sweep, and the statistical average
is taken over 4 independent runs starting from
diﬀerent random initial conﬁgurations.
It is found that the zero-ﬁeld topological chiral order with the miniature SkX structure isrelatively robust against easy-axis (D < 0)and easy-plane (D > 0) anisotropies, although
for strong easy-axis (easy-plane) anisotropy, a
triple-Q collinear (coplanar) state is favored.
Since the SkX and anti-SkX each having positive or negative chirality are energetically degenerate, the topological Hall eﬀect of alternative sign is possible at zero ﬁeld. For moderate291

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of TokyoD < 0 (D > 0), the collinear (coplanar) phasepreempts the SkX phase and in the chiral sector, it shows a random domain structure consisting of positive- and negative-chirality clusters [4]. It is also found that the miniature SkX
is robust against the external magnetic ﬁeld,
but further analysis is necessary to clarify the
in-ﬁeld properties of the model.

References[1] T. Okubo, S. Chung, and H. Kawamura,Phys. Rev. Lett. 108, (2012) 017206.
[2] R. Ozawa, S. Hayami, and Y. Motome,
Phys. Rev. Lett. 118, 147205 (2017).
[3] K. Aoyama and H. Kawamura, Phys. Rev.
B 105, (2022) L100407.
[4] K. Aoyama and H. Kawamura, J. Phys.
Soc. Jpn. 92, (2023) 033701.

292

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Molecular modeling of ferroelectric nematic phase
Takeaki ARAKI, and Matheus DE MELLO
Department of Physics,
Kyoto University, Kitashirakawa-Oiwake-cho, Sakyo, Kyoto 606-8502
Ferroelectric liquid crystals are partially

The distribution function suggests the molecule

ordered anisotropic fluids with ferroelectricity.

shape can be regarded as a rod without theIts experimental realization has not beenremarkable head-tail asymmetry. Owing to thereported for a long time. Recently, someelectric charges, the local parallel-parallel

experimental

inalignment is favored than the parallel-anti

synthesizing molecules which possibly show

parallel alignment, which is usually observed inferroelectric nematic order [1-2].

the conventional nematic phase.

groupshave

succeeded

Even though the molecular features required
to stabilize the ferroelectric phase weresystematically studied, the origins of the
spontaneous polar ordering remain still unclear.
In this work, we study a molecular liquid DIO,
which were synthesized by Nishikawa et al.,focusing on which of the dipole-dipole
electrostactic interaction or the polar shape ofthe molecules is dominant for the polar ordering.
We performed all-atom molecular dynamics
simulations of 1000-2000 DIO molecules with

Fig. 1: (a) A snapshot of the liquid crystal phase

GROMACS. The partial charge on each atom isof DIO at 330K. The molecule is represented

estimated by using Gaussian. In order to see thewith a rod whose color shows the orientation. (b)effect of the electrostatic interaction, we carried

The distribution function. (c) The correlationout the simulations of the DIO without thefunction.

electric charges. We analyzed the nematic, polar

References

order parameters, the spatial distribution, and the[1] H. Nishikawa, et al. Adv. Mater. 29,

correlation between the molecular axes.

1702354 (2017).

Our

simulation

results

indicate[2] R. J. Mandle, et al., Chem. Eur. J. 23, 14554

the(2017).

spontaneous polar ordering at a low temperature.

293

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Aggregation of polyelectrolytes in mixed solvents
Takeaki ARAKI, and Dai Matsubara
Department of Physics,
Kyoto University, Kitashirakawa-Oiwake-cho, Sakyo, Kyoto 606-8502
Polymer systems often exhibit phase-

homogeneously. As its composition is decreased

separated structures with diverse patterns from(top), the system is phase-separated and a

microscopic to macroscopic length scales. In

labyrinth pattern is formed. In the polymer-richparticular,

phase, a stripe concentration modulation pattern

diblock

copolymer

melts

and

polyelectrolyte in poor solvents are expected tois observed.

be applied to nanostructures.
In this study, we consider mesoscopic patternformation of weakly charged polyelectrolytes inbinary mixture solvents by means of field
theoretic polymer simulations in lattice space.
The polymer is modeled as a continuous
Gaussian chain, and is described by path
integrals.

The

solvent-polyelectrolyte

andsolvent-solvent interactions are given by Flory
Huggins

parameters.

Theelectrostaticinteraction is obtained by solving PoissonBoltzmann equation. The binary solvent itself ismixed. But the affinities of the solvents to thepolyelectrolyte is different. One of the solvents
prefers the polyelectrolyte more than the other.

Fig. 1: Snapshots of the phase-separated patternFigure 1 shows the concentration field of theof the polyelectrolyte in binary mixture solvents.

polyelectrolyte in the binary solvent mixtures.

The concentration of the more preferred

The composition of the more preferred solvent

component is increased from top to bottom.

is increased from top to bottom with fixing theReferences

polyelectrolyte concentration. When the more

[1] G. H. Fredrickson, The Equilibrium Theoryof Inhomogeneous Polymers, Oxford
University Press, 2013.

preferred component is rich in the solvent(bottom),thepolyelectrolyte

is

mixed

294

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Rheology Simulation of Dynamically Cross-linked Networks
Yusuke Yasuda
Research Center for Computational Design of Advanced Functional Materials.

National Institute for Advanced Industrial Science and Technology, Ibaraki 305-8568.
Recently a new type of dynamic bondelastomers called entropy-driven has gained
attention for showing superior self-healing andtoughness compared to conventional dynamic
bond elastomers [1], but detailed understanding is
not reached yet. Last year, we succeeded infabricating a new coarse-grained model which
can reproduce typical hysteresis and self-healing
properties of conventional dynamic bondelastomer. This year, we introduced entropydriven properties to the coarse-grained model.
In this work, we made coarse-grained modelfor dynamic bond elastomers based on KremerGrest model [1]. For cross-links between reactive
bead, we introduced harmonic potential withassociating and dissociating condition (Fig. 1).
We tuned harmonic constant K and bond fractureenergy εd. Under this condition we conductedequilibration simulation and mechanical tests.
Calculation was conducted using molecular
dynamics simulation software packages OCTA,
and LAMMPS on ISSP Supercomputer System.

Fig. 2, the relationship among Bond lifetime,
dissociation energy, and harmonic constant K
We also conducted lifetime analysis fordifferent Ks. Then we found that average bondlifetime follows Arrhenius equation depending on
K (Fig. 2), and dynamic bonds with smaller K
shows shorter lifetime in high εd region,
concluding that entropy-driven dynamic bondelastomers with high εd owing to smaller
dissociation activation energy.
We also conducted mechanical and stressrecovery test. Both models show typical large
hysteresis, and stress recovery at hightemperature. Then we analyzed stress-relaxation
behavior by analyzing relationship between stressand healing time, to find that stress-relaxation
times are closely related to bond lifetime. Then
we conclude that faster self-healing in entropydriven dynamic bond elastomers originates fromfaster dissociation / association dynamics, whichcomes from lower activation energy fordissociation [3].
References [1] Kim et al. Macromolecules
2020, 53, 4121. [2] K, Kremer et al., J. Chem.
Phys., 1990, 92, 5057. [3] Y. Yasuda et al.,under review.
Acknowledgement This work is partially supported
by a project JPNP18016, commissioned by the New
Energy and Industrial Technology Development
Organization(NEDO)

Fig.1, Schematic illustration of the coarsegrained model of dynamic bond elastomers.
From equilibration simulation, we found thatequilibrium constant Ka has proportional
dependence to 1/T, consistent to Gibbs equation.
From the slopes and y-intercepts of the 1/T
dependence of Ka, we defined ΔSa and ΔUa. From
this analysis, we found that ΔUa are influenced
primarily by εd, and ΔSa are predominantly
influenced by on K. As K decreases, ΔSa
increases, confirming smaller K represents
entropy-driven systems.

295

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Structural Analysis and Mechanical Properties Simulations ofPolymer Networks with Various Network Structures
Yusuke Yasuda
Research Center for Computational Design of Advanced Functional Materials.

National Institute for Advanced Industrial Science and Technology, Ibaraki 305-8568.
Rubber elasticity theories proposed so far
qualitatively reproduces the mechanicalproperties of vulcanized rubbers and crosslinked gels, but it is not quantitative. One reasonof this is that the actual polymer networks
contain inhomogeneity, and it is difficult to
detect it by experiments. Then theoretically,
homogeneous network is assumed.
In this work, we aimed to develop amethodology to handle network inhomogeneity,
focusing on detecting structural defects in the
network. We performed coarse-grained
molecular dynamics simulations for networks
with various inhomogeneity. Then we analyzed
the structure during the crosslinking process andthe mechanical properties under uniaxial
deformation. We tried to extract substructuresthat contribute to the mechanical properties.

inhomogeneity, we terminate the reaction
calculation when reaction ratio reaches certain
value in range of 0.24 – 0.96. Then we
conducted uni-axial elongation test (λ=1-4) , andobtain stress-extension ratio curves. Then we
calculated elastic modulus Gsim by fitting theinitial shape of stress-extension ratio curves.
70 x 10-3

Arm length = 5 σ
3-arm
4-arm
Arm length = 10 σ
3-arm
4-arm

3

Gsim [ ε/σ ]

60
50
40
30
20
10
0
0

5

10

15

20

3

Ncycle, eff [ /σ ]25

30 35 x 10-3

Fig. 2 Elastic moduli Gsim against the numberof effective closed loops Ncycle,eff.
Then we tried to calculate the structure which
contribute to elastic moduli. Based on the
Scanlan-Case [2,3] criterion, we analyzed the
network structure. We counted only effective
chain s between branching points with three ormore branches, excluding dangling, loop, superdangling, super-loop structure and eliminate,including bridge and super-bridge structures. In
the network constructed by effective chains, wecounted the number of closed loops in network
based on phantom network theory, finding thatelastic moduli are proportional to Ncycle,eff for all
branch num and arm length.
References 1. 1. Fujiyabu et al., Sci Adv. 2022,
8(14), eabk0010 2. Scanlan, J. Polym. Sci., 1960,
43, 501, 3. Case, J. Polym. Sci., 1960, 45,
Acknowledgement This work is supported by aGrant-in-Aid for Young Scientists (22K14740)

Fig, 1. Schematic illustration of thefabrication method of the system.
In this research, we fabricated coarse-grainedmodel of star-shaped end-crosslinked elastomers
which is known as a homogeneous network, wascreated based of the repulsive Kremer-Grest
model. We prepared 3-arm and 4-arm systems
referring to tetra-PEG, tri-PEG gel [1] tuning thearm length. Two terminal types A, B are
prepared. Total number of beads in system are
set approximately 1.0×105.τ density conditionis set as 0.85σ-3Then system relaxation
calculation and reaction calculations. For
fabrication of networks with various

296Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Theory of microwave scattering in a dissipative
superconducting circuit element
TSUYOSHI YAMAMOTO
Faculty of Pure and Applied Sciences, University of Tsukuba
Tsukuba, Ibaraki 305-8571==

Figure 1: (top) A Josephson junction with theJosephson energy EJ and the charging energy
EC shunted by a resistor with the resistanceR. (bottom) Illustration of the phase diagramof the Schmid-Buldagaev transition.
A Josephson junction is a building blockof superconducting circuits. Thanks to its
characteristic nonlinearity, we can develop
a great variety of superconducting devices,such as a superconducting qubit and a parametric amplifier. Although the property of
the isolated Josephson junction is understoodwell, it is little known, especially its manybody eﬀects, when it contacts electromagnetic environments. The most notable phenomenon is a dissipation-induced quantum
phase transition. Forty years ago, Schmidand Buldagaev theoretically found that a single Josephson junction shunted to a resistor, a resistively-shunted Josephson junction,

undergoes a superconductor-insulator transition [1, 2]. When the resistance is larger thana resistance quantum (R > RQ = h/4e2 ),the Josephson junction becomes an insulator,
while when R < RQ , it becomes a superconductor, interestingly not being controlled byjunction parameters (see Figure 1).
Although the theoretical works on the DC
transport checked the Schmid-Bulgadaev transition [3, 4], there is no decisive evidence forthe experimental observation [5], stirring up
controversy. The recent development of superconducting circuits allows experimentalists tomeasure the AC-impedance of the resistivelyshunted Josephson junction using microwave
spectroscopy and then to obtain important
properties, such as the excitation spectrum or
the dynamical property. This endeavor would
be a key to solving the ongoing debate problem.
In this project, we numerically investigatedthe AC-impedance of the resistively-shunted
Josephson junction [6]. To obtain the ACimpedance, we calculated the phase-phase correlation function defined on the imaginarytime axis using the path-integral quantum
Monte Carlo (PIQMC) simulation and thenperformed the analytical continuation for the
PIQMC data using the Padé approximation.
Since the Padè approximation needs highlyaccurate calculations for the applying data,
we performed the parallel computing for theMonte Carlo sampling mainly using “F2cpu”,
succeeding in collecting a large enough Monte297

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo[4] S. L. Lukyanov and P. Werner, J. Stat.
Mech. P06002 (2007).
[5] A. Murani et al., Phys. Rev. X 10, 021003(2020).
[6] P. Werner and M. Troyer, Phys. Rev. Lett.
95, 060201 (2005).
[7] T. Yamamoto, T. Kato, L. I. Glazman,
and M. Houzet, in preparation.

Figure 2: Frequency dependence of the realpart of the AC-impedance Z ′ (ω) for various
values of α = RQ /R = 2 (red), 1 (green), and
1/2 (blue), obtained by the PIQMC. At theDC limit (ω → 0), the AC-impedance goes to
0 and R for α > 1 and α < 1, respectively,
which is a signature of the Schmid-Bulgadaev
transition. The dashed lines represents the analytical formulas.
Carlo steps, typically ∼ 5 × 107 , to provide theaccurate calculations for the AC-impedance.
Our numerical results for the AC-impedanceas a function of frequency or Matsubara frequency (the Fourier space of the imaginarytime) confirmed the superconductor-insulatortransition, as originally predicted by Schmidand Bulgadaev and revealed the low- and highenergy physics and their crossover (see Figure 2) [7]. This work would give a deep understanding of the dissipative Josephson junction,
which pushes further development for superconducting devices using Josephson junctions.

References[1] A. Schmid, Phys. Rev. Lett. 51, 1506
(1983).
[2] S. A. Bulgadaev, JETP Lett. 39, 264(1984).
[3] N. Kimura and T. Kato, Phys. Rev. B 69,
012504 (2004).

298

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Grain boundary Li ion conductivity in NASICON-type Solid
Electrolytes
Masanobu NAKAYAMA
Department of Advanced Ceramics,
Nagoya Institute of Technology, Gokiso, Showa-ku, Nagoya, Aichi 466-8555
LiZr2(PO4)3 (LZP) related materials arecomposition, but (a) with Ca ions randomly

promising solid electrolyte materials with higharranged and (b) with Ca ions segregated at theionic conductivity and chemical stability.[1]grain boundary, as experimentally confirmed.

Many material calculations have been applied

For these models, molecular dynamics was

to lithium ion-conducting NASICON-type solid

performed to evaluate the diffusion coefficientelectrolytes, but most of them are concerned

versus temperature, and the results are shown inwith bulk conduction, and grain boundaries

Figure 2 as an Arrhenius plot. The obtained

have rarely been investigated.[2], [3] In thisresults show that the ionic conductivity (roomstudy, the effect of dopant Ca on the ionictemperature) of the segregated structure (b) is 5

conductivity is discussed.

x 10-4 cm2 s-1, which is about twice theTo calculate lithium ion diffusion in the

diffusion coefficient of the random structure (a).

grain boundary model of LZP, we used theOn the other hand, no significant change in

Bond Valence Force Field (BVFF) proposed by

activation energy was observed. This suggests

Adams et al.[4] plus a Stillinger-Weber (SW)that the grain boundary conductivity can be

type

controlled by controlling the arrangement of

force

field

representing

three-bodyinteractions. Parameters included in the force

dopants.

field were evaluated by a cuckoo search to

A more detailed analysis of the obtained results

match the results of first-principles calculations.

shows that Li ions have a trapping effect near

The same was employed: force-field molecular

Ca ions. In previous studies, the grain boundary

dynamics (FFMD) calculations using the NVT

structure also has the effect of trapping Li ions.

ensemble were performed for 1 ns every 50 K

Therefore, it is suggested that Li ions become

from 400 K to 900 K. The results are presented

excessively

in Table 1. The software package NAP[5], [6]boundary. We believe that such excess of

was used for the above.

lithium ions contributes to the enhancement of

Figure 1 shows the grain boundary model usedionic conduction at the grain boundary.

in this study. (a) and (b) have the same299

concentrated

near

the

grain

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Figure 1 Grain boundary structure model for Ca-doped LZP. (a) Random substitution of Ca to Zr
sites and (b) Ca segregation model near grain boundaries.

Figure 2 Comparison of diffusion coefficients of Li in two-types of grain boundary models of Cadoped LZP. (Black symbol: random Ca arrangement model; Green symbol: Ca segregation modelnear to grain boundary.

References[4] S. Adams et al., Phys. Stat. Sol. A, 208,

[1] M. Nakayama et al., Chem. Commun., 58,

1745-1753 (2011)

9328–9340, (2022)[5] R. Kobayashi, J. Open Source Softw.,6,[2] K. Nakano et al., J. Phys. Chem. C, 125,

2768, (2020)

23604–23612 (2021)[6] R. Kobayashi et al., APL Mater., 8, 081111,

[3]

R.

Kobayashi

et

al.,

Acta

Mater.,(2020)

226,117596 (2022)

300

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Surface Tension Stabilizing Cell Motion Order
Katsuyoshi Matsushtia, Naoya Kamamoto, Maki Sudo, Taiko Arakaki
Depertment of Biological Science, Graduate School of Science, Osaka University
Machikaneyama, Toyonaka, Osaka 560-0043

Cells collectively move in particular scaffolds in various biological phenomena, including organ morphogenesis [1]. For example,
neuronal cells move by interacting with surrounding glial tissue[2]. The collective motion
reflects the interaction between the tissue andthe surrounding scaffold. The ordering mechanism in this collective motion needs sufficientexamination through the computer simulation
based on statistical physics. In particular, theessential interaction in statistical physics is thesurface tension between the cells and scaffolds,
which should be incorporated into the simulation. To clarify the effect of the interface
tension, we consider a collective motion of acell cluster and its dependence on the surface
tension.

This result explains the previous result of
the cluster consisting of the two types of cells,
which is reported in the previous project [6].
The cell cluster transitions from the rotational
to straight motion as the differentiation of cells
progresses. The present result implies that thismotion transition originates from the surface
tension decreases associated with the cell typechanges in the differentiation.
We appreciate Prof. H. Kuwayama, Prof.
S. Yabunaka, Dr.
H. Hashimura, Prof.
Sawamoto, Dr. Matsuda, Prof. H. Yoshino,
and Prof. M. Kikuchi their various support on
this work.

We employed the cellular Potts model [3]and extended its cells with the polarity of cellcell adhesion [4]. This model induces the spontaneous order of the cellular motion and successfully explains the collective migration ofcells [5]. We changed the surface tension between the scaffold and the cells and observed
the collective motion. In the case of low surface
tension, the cells have an affinity with the scaffolds and individually exhibit a random walk.
When the surface tension increases above theGraner-Glazier surface tension, the cells forma cluster and collectively move, In this case,the collective motion has a persistence time,in which the cluster almost straightly moves.
As the surface tension increases, the switchingfrom the straight motion to the rotational motion and inverse switching appears. In the caseof large surface tension, the collective motion
becomes the rotation, the center of which corresponds to the center of the cluster. Due tothe rotational motion, the center of the cluster
exhibits only diffusion.

[1] P. Rørth, Ann. Rev. Cell Develop. Biol.
25, 407 (2009).

References[2] C. Nakajima, M. Sawada, K. Sawamoto,Curr. Opi. Neurobio. 66, 1 (2021).
[3] F. Graner and J. Glazier, Phys. Rev. Lett.
69, 2013 (1992).
[4] K Matsushita, Phys. Rev. E 95, 032415(2017).
[5] K Matsushita, Phys. Rev. E 97, 042413(2018).
[6] K. Matsushita, N. Kamamoto, M. Sudo,
K. Fujimoto, Proc. Sympo. Traffic Flow
Self-driven Parti. 27, 23 (2021).

301

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Calculation of ordered structures, dynamics and opticalproperties of soft materials
Jun-ichi FUKUDA
Department of Physics, Kyushu University, Motooka 744, Nishi-ku, Fukuoka 819-0395
We continued our study that started in theelectromagnetic field of light. We employ

previous year on the structure of a cholestericplane-wave expansions in the directions normal

blue phase liquid crystal (BPLC) under anto the thickness direction, and the Maxwell

electric field. Our study was motivated by aequations were discretized so that the matrix

recent experimental study [1] demonstrating therepresenting the Maxwell equation is of blockformation of various (meta)stable ordering withtridiagonal form.

different periodicity in a reproducible manner

Our calculations [2] showed that structures

by applying an electric field. Particularly, awith different and discretized periodicity are

tetragonal structure with I4122 symmetry was

indeed (meta)stable. The free energy evaluation

shown to be (meta)stable even after theof these structures revealed that the periodicity

cessation of the electric field, which had notofbeen reported.

systematically on the electric field strength. We

the

most

stablestructuredepends

Our numerical study focused on the role ofalso calculated [2] the reflection spectra of theplanar surfaces that confine BPLC on its

structures. We found strong selective reflection

structure and stability, and was based on thedepending on the circular polarization of the

Landau-de Gennes continuum theory thatincident light arising from the chirality ofdescribes the orientational order by a second

BPLC, and the variation of the reflection peakrank tensor. Calculations were carried out on a

position in response to the electric field.

regular

mesh,

which

facilitatedtheparallelization (in our case OpenMP was used).

References

We also calculated the optical properties of the[1] D.-Y. Guo et al., Nature Mater. 19, 94

structures obtained (more specifically, how(2020).

monochromatic incident light with a given[2] J. Fukuda, Front. Soft Matter 2 1011618

wavevector is reflected and transmitted) by(2022).

directly solving the Maxwell equations for the302

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Measurement-induced phase transitions inU (1)-symmetric monitored circuits
Yohei FUJI
Department of Applied Physics, University of Tokyo, Tokyo 113-8656

Many-body quantum systems evolved under repeated measurements have recently beenshown to reveal a novel type of nonequilibrium phase transition called the measurementinduced phase transition [1, 2]. A paradigmatic example is the entanglement transition from a volume-law to an area-law entangled phase, which is induced by the competition between unitary dynamics and projective measurements. Surprisingly, the entanglement transition exhibits critical phenomena described by conformal field theory as inequilibrium phase transitions. It is then natural to ask whether universality classes due
to symmetry or dimensionality also arise inmeasurement-induced phase transitions.

ground state of one-dimensional quantum systems preserving U (1) symmetry. This result thus highlights the importance of symmetry for classifying and understanding themeasurement-induced phase transitions.

In this study, we considered one-dimensionalrandom quantum circuits consisting of twosite unitary gates and projective measurementsof single qubits, both of which are design topreserve a U (1) charge corresponding to thetotal number of flipped qubits [3]. By employing parallel sampling implemented in Julia on Supercomputer System B at ISSP, we
computed steady-state physical quantities averaged over many circuit realizations and measurement outcomes. In addition to the entanglement transition characterized by the logarithmic scaling of the entanglement entropy,
we found a charge-fluctuation transition characterized by the logarithmic scaling of the bipartite charge fluctuation as shown in Fig. 1.
The latter transition exhibits critical phenomena akin to the Tomonaga-Luttinger liquid
theory, much like critical phenomena for the

Figure 1: Subsystem charge correlation function ⟨nA nC ⟩c between two disjoint subsystems
A and C is plotted as a function of the measurement rate p. It is related to the bipartite
charge fluctuation FA by ⟨nA nC ⟩c = FA +FC −FA∪C .

L=8

0.50

L=12
L=16
L=20

0.20

L=24

0.10 0.8
0.05
0.02

0.6
0.4
0.2
0.0

0.01
0.00

- 0.5

0.0

0.05

0.5

0.10

1.0

0.15

0.20

0.25

0.30

References[1] A. C. Potter and R. Vasseur, Entanglement Dynamics in Hybrid Quantum Circuits in Entanglement in Spin
Chains: From Theory to Quantum Technology Applications, edited by A. Bayat,
S. Bose, and H. Johannesson (Springer
International Publishing, Cham, 2022)
pp. 211–249.

303

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo[2] M. P. A. Fisher, V. Khemani, A. Nahum,and S. Vijay, Random Quantum Circuits,
Annu. Rev. Condens. Matter Phys. 14,335 (2023).
[3] H. Oshima and Y. Fuji, Charge fluctuation and charge-resolved entanglement ina monitored quantum circuit with U (1)symmetry, Phys. Rev. B 107, 014308(2023).

304

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First principles studies of electronic structuresin armchair-edged graphene nanoribbons
Junhuan LI, Kenta ARIMA
Graduate School of Engineering, Osaka University, Yamada-oka, Suita, Osaka 565-0871
Local electronic structures of graphene

The band structure of 39-AGNR (where 39 is

nanosheets have been intensively studied so far

the width measure by the number of rows of[1, 2]. However, the relationship between the

carbon atoms across the AGNR) is shown in theelectronic structure and atomic arrangement isright panel of Figure 1. Based on the simulated

still unclear. In this study, we simulated scanningband structure, the STM images were calculated

tunnelingofusing different sample biases. At a sample bias

nanoribbonsof -1 V, the simulated STM image exhibit

microscopy

armchair-edged(STM)graphene

images(AGNRs) by the first principles calculations.

hexagonal patterns as shown in Figure 2.

Calculations were performed by STATE(Simulation Tool

for AtomThis work has been performed on System B

TEchnology)of the Supercomputer Center, the Institute for

package. The AGNR we simulated was

Solid State Physics, the University of Tokyo.

terminated by hydrogen atoms as shown in
Figure 1. Periodic boundary conditions wereapplied to a supercell setup with length 𝑙.

Fig. 2: The simulated STM image of 39-AGNR.

References[1] T. Wassmann et al., J. Am. Chem. Soc. 132,
Fig. 1: The ribbon configuration and band

3440 (2010).

structure of 39-AGNR.

[2] J. Li et al., Phys. Rev. B 103, 245433 (2021).

305

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Numerical studies on thermodynamics and excitation
spectra in Heisenberg antiferromagnets on thetriangular and kagome lattices
Yoshiyuki Fukumoto
Department of Physics, Faculty of Science and Technology, Tokyo University of Science
2641 Yamazaki, Noda, Chiba 278-8510

Neutron scattering experiments on twodimensional frustrated spin systems with spontaneous magnetizations, such as a triangularlattice system Ba3 CoSb2 O9 [1] and a kagomelattice system Cs2 Cu3 SnF12 with the q =0 type ordering stabilized by DzyaloshinskiiMoriya (DM) interactions [2,3], have revealed
that the dynamic structure factors consist of
not only collective magnon excitations but also
spinon continuums.
As for the triangular-lattice system, Zhang
and Li used the fermionic spinon operator to
calculate dynamic structure factors within the
RPA approximation [4]. They argued that thestructure of the spinon continuum is closely
related to the roton minimum of the magnon
spectrum, and also found an amplitude mode
composed from two spinons that cannot be
captured by the spin wave approximation.
As for the kagome-lattice system, magnon
dispersions obtained from a neutron scattering experiment on Cs2 Cu3 SnF12 were analyzed
based on the linear spin wave (LSW) approximation, and reported that the spin-wave interactions led to a large negative-renormalization,
where the exchange parameter was found to
be about 40% smaller than a reliable value,
J = 20.7 meV, determined from magnetic susceptibility measurements [2]. Also, a recent
neutron scattering experiment has observed afourth collective excitation that is thought tobe an amplitude mode [3].
In this article, we report our recent results of an Ising expansion (IEP) study on
the large negative-renormalization observed in

306

Cs2 Cu3 SnF12 . In order to stabilize the q = 0
ordering, which is assumed in the xy plane,
we incorporate the DM interaction with the Dvector of D = (0, 0, D∥ ) and write a minimal
Hamiltonian for this material as
H=J

∑
⟨i,j⟩

Si · Sj +∑D · (S i × S j ),

(1)

⟨i,j⟩

where S i represents the S = 1/2 operator atsite i. Introducing a perturbation parameter
λ, we write Hλ = H0 + λH1 , where H0 is
an Ising model and we assume λ → 1 recovers eq. (1). The perturbation calculation was
performed using the connected cluster expansion method, including all clusters up to 9 links
and 10 sites. Multi-block diagonalization was
performed in the calculation of the eﬀective
Hamiltonian [5]. We also incorporated a local field term to improve convergence of theseries [5]. The local field term is the form of
staggered magnetic field stabilizing the q = 0
ordering and its strength is defined as t. We
include the local field term into H0 and thatwith opposite sign into H1 , so as to cancel out
with each other at λ → 1. If we set t = 0,then we have unphysical results such as themagnon energies being negative in most of the
wave vectors. When t > 0, the results change
significantly at first, and the region of negative
magnon energy decreases. When t increases tosome extent, the region of negative magnon energy disappears and the result does not depend
on t very much. As such value of t, we choose
t/J = 1 in this study. As t is further increased,
the resulting t dependence increases slowly, be-Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Figure 1: Renormalization factors rk,α (λ).
The black and red lines, respectively, represent
the ninth and eighth order results.

Figure 2: Comparison of magnon dispersions
between the IEP and LSW. The choice of line
type is the same as in Fig. 1.

cause it becomes harder to correctly handle thelocal field term included in the perturbation.
Let ϵk,α be the magnon excitation energy
to be obtained, where k is the wave vector
and α is the identifier for three modes in the
kagome lattice. We set ϵk,α = rk,α (1) × ϵLSWk,α ,blue lines are, respectively, the ninth-order IEP
and LSW results, where D∥ /J = 0.12. On
the other hand, the red lines are the results of
the LSW with renormalized parameters whereD∥ /J = 0.18 and the energy scale is modified
by a factor 0.62 [2]. Figure 2 tells us that thedispersion relations calculated by the LSW undergo not only renormalization of the interaction parameters but also flattening of the highenergy part and the appearance of roton-likeminimum around M point due to spin-wave interactions. As for the triangular lattice system,
we had modification of high-energy part but norenormalization of interaction parameters [5].
In summary, we have used the IEP to investigate how spin-wave interactions modify the
LSW predictions of magnon dispersions. We
have successfully calculated the renormalization factor rk,α (λ) by the IEP. As a result, inthe kagome lattice system, in addition to therenormalization of the interaction parameter
pointed out by Ono et al. [2], we have foundthat the high-energy part around M point undergoes strong deformation. In the near future, we plan to incorporate more realistic DM
interactions to reproduce the detailed structure of experimental magnon spectrum data.

where ϵLSWk,α is the magnon excitation energy inthe LSW and rk,α (λ) is “renormalization factor”. We calculate rk,α (λ) by the IEP up to
the ninth order, and evaluate value of rk,α (1)by naive summation of the obtained series. In
Fig. 1 we show the eighth and ninth order results of rk,α (λ) at Γ and M points as a functionof λ, and find a good convergence. In particular, for M point, the renormalization factor
represented by the solid line amounts to 0.26 at
λ → 1, which demonstrates that large negative
renormalization occurs in the kagome system.
Experimental results for Cs2 Cu3 SnF12 showthat the two high-energy modes are almost degenerate at the Γ point, and their energies areabout 10 meV [2,3]. By fixing J = 20.7meV,
which has been determined from magnetic susceptibility measurements [2], and searching thevalue of D∥ so that the calculated energy ofthe high-energy modes at Γ point is 10 meV,
we obtain D∥ /J = 0.12.
Figure 2 shows a comparison of the magnon
dispersions between the IEP and the LSW.
The path on the wave-vector plane, which connects Γ and M points, is chosen in the same
way as in the experiment [3]. The black and307[1] S. Ito et al., Nat. commn. 8, 235, 2017.
[2] T. Ono et al., JPSJ 83, 043701, 2014.
[3] M. Saito et al., PRB 105, 064424, 2022.
[4] C. Zhang et al., PRB 102, 075108, 2020.
[5] W. Zheng et al., PRB 74, 224420, 2006.

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Numerical study of the eﬀect of large bondrandomness on the magnetization process andmagnetic susceptibility in the spherical Kagome
system {W72V30}
Yoshiyuki Fukumoto
Department of Physics, Faculty of Science and Technology, Tokyo University of Science
2641 Yamazaki, Noda, Chiba 278-8510

The spherical kagome system {W72 V30 } is afrustrated cluster magnet consisting of thirty
V4+ with S = 1/2 [1]. The exchange interaction has been estimated as J = 115K. In
the experimental study of the low-temperaturemagnetization process, a step structure uniqueto cluster magnets has not been found [2]. To
understand this result, the eﬀects of bond randomness [2] and Dzyaloshinskii-Moriya (DM)interactions [3] were examined.
Subsequently, specific heat measurements
were performed by Kihara et al. at temperatures T < 11K and magnetic fields up to
15T [4]. Although the magnitude of the specific heat is comparable to that expected in
the Heisenberg model, the shape of the specific heat curve is completely diﬀerent. In particular, in the Heisenberg-model calculation, apeak appeared near 2K, and it was found thateven a magnetic field up to 15T aﬀected theshape of the specific heat curve. However, inthe experiment, the specific heat curve had nopeak and was not aﬀected by the magnetic field
up to 15T. To resolve this problem, the specific heat calculation was performed considering bond randomness or DM interaction. Eachstrength is assumed to be about 0.1J. However, it did not lead to an explanation of theexperimental results.
In a previous study investigating the eﬀect ofbond randomness in a triangular lattice system[5], it was reported that when the randomness
is large compared to the energy scale of the308

magnetic field, the magnetic field dependenceof the specific heat disappears. Therefore, we
decided to investigate the eﬀect of strong bondrandomness also in the spherical kagome system. As a result, it was clarified that the experimental results can be explained by assuming that the strength of the bond randomness
is 40-50% of the exchange interaction. The results were published as a paper [6].
It is also interesting to study if the model
Hamiltonian with the large bond randomness
and a modest DM interaction reproduces experimental magnetic susceptibility. Magnetic
susceptibility calculation in the presence of theboth terms is a challenging problem in termsof computational complexity. If we have DM
interactions, we need to work with vectors ofdimension 230 . In addition, when DM interaction exists, the magnetic susceptibility is obtained by applying a small magnetic field andcalculating the expected value of magnetization by the cTPQ method. Then, it is necessary to average the direction of the magnetic
field, the bond randomness, and the random
vector of the cTPQ method. We will continue our research on eﬃcient implementationof these averaging operations.
[1] A. M. Todae et al., ChemComm, 3351 (2009).
[2] J. Schnack et al., arXiv:1304.2603v1.
[2] Y. Fukumoto et al., JPSJ 87, 124710 (2018).
[4] T. Kihara et al., PRB 99, 064430 (2019).
[5] K. Watanabe et al., JPSJ 83, 034714 (2014).
[6] M. Motohashi et al., PTEP, 113I01 (2022).

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Phase separation dynamics in hydrodynamic models
Kyosuke ADACHI

Nonequilibrium Physics of Living Matter RIKEN Hakubi Research Team, RIKEN
Center for Biosystems Dynamics Research (BDR), 2-2-3 Minatojima-minamimachi,
Chuo-ku, Kobe 650-0047, JapanRIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS),
2-1 Hirosawa, Wako 351-0198, Japan
Liquid-liquid phase separation (LLPS),

Slyozov-Wagner (LSW) scaling for the growthwhich is widely recognized in condensedof the cluster size. Then, as a simple extension

matter and soft matter physics, has gained

to nonequilibrium conditions that can be

renewed attention in cell biology. Several kinds

relevantof protein condensates have been found in aconsidered a model that breaks the fluctuation-

cell, and LLPS is discussed as a possible

dissipation relation. This model is similar tomechanism of the formation and dynamics ofActive Model B+ [3], which have beencondensates [1]. To understand how the amino

originally proposed as a model of active matter.

acid sequence of proteins and intracellular

Performing numerical simulations with small

nonequilibrium environments can affect theadditional terms to improve numerical stability,phase

we found that the cluster growth is slower thanseparation

simulationsofdynamics,
polymer

numerical

modelstointracellularsituations,wethat in Model B, suggesting the suppression ofandthe phase separation dynamics due to thehydrodynamic models should be useful.

nonequilibrium conditions.

This year, as a preliminary study, we
performed several numerical simulations ofhydrodynamic models, which are PDE models

Referencesof the particle density field. We first tested the[1] S. F. Banani et al., Nat. Rev. Mol. Cell Biol.

Model B dynamics [2] by the Euler-Maruyama

18, 285 (2017).

method with discretization of time and space.

[2] P. C. Hohenberg and B. I. Halperin, Rev.

Using parallel computing to collect many

Mod. Phys. 49, 435 (1977).

samples with different initial conditions and[3] C. Nardini et al., Phys. Rev. X 7, 021007

noise realizations, we confirmed the Lifshitz-(2017).

311

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Solution of the Eliashberg equation with theelectron-electron Coulomb interaction
Ryosuke AKASHINational Institutes for Quantum Science and Technology
The Eliashberg theory[1] with the Migdal
approximation [2] is the state-of-the-art framework for the first-principles calculations ofphonon-mediated superconductors. In principle, the competition between the phononmediated pairing attraction and screened
Coulomb repulsion is accurately treated there
for weakly correlated metals, which is crucial
for reliable estimation of the superconducting
transition temperature Tc . However, the latter part has been mostly treated as a single
empirical parameter µ∗ [3], with which one cannot apply the theory to undiscovered systems
where the Tc measurement does not exist. It
is only recent that the effect of the screened
Coulomb interaction on the pairing has been
explored non-empirically.
The author has recently studied the effect of
frequency dependent screened Coulomb interaction within the random -phase approximation. He has revealed the cooperative mechanism of the plasmonic and phononic pairing
attractions from first principles, by analysing
the non-diagonal (pairing) part of the self energy [4, 5]. On the other hand, a part of diagonal self energy due to the plasmons were shownto have negative effect, partially cancelling theformer pairing [6]. However, the full treatment of all the components of the screened
Coulomb-induced frequency dependent self energy was missing. In this project, we calculated
the Eliashberg equations with all the screened
Coulomb self energy parts included for uniform
electron gas [7].
The Eliashberg equations with the Migdal

approximation are written as follows:
Zi (iωj ) = 1 − T

X ωj 0 Zi0 (ωj 0 )i0 j 0χi (iωj ) = T

ωj Θj 0 (iωj 0 )X ξi0 + χi0 (ωj 0 )i0 j 0φi (iωj ) = −T

Θj 0 (iωj 0 )X φi0 (ωj 0 )i0 j 0Θj 0 (iωj 0 )Wii0 (ωj − ωj 0 ), (1)Wii0 (ωj − ωj 0 ),Wii0 (ωj − ωj 0 ),

(2)
(3)

Θi (iωj ) = [ωj Zi (iωj )]2 +[ξi + χi (iωj )]2 +[φi (iωj )]2 .
(4)
Indices i (i0 ) and j (j 0 ) denote the basis stateand Matsubara frequency, respectively. ξi isthe energy eigenvalue of the state i measured
from the chemical potential. Wii0 (νj ) is thematrix element of the interaction between the
Cooper pair states labeled by i and i∗ with the
Bosonic Matsubara frequency νj .
For homogeneous electron gas the basis icorresponds to the plane-wave state. We have
implemented a code package that solves theabove equations with an approximate manner. The interaction Wii0 (νj ) was set to thescreened Coulomb interaction Wel within the
random phase approximation plus the attraction mediated by the Einstein phonon with definite frequency and electron-phonon coupling
Wph . The diagonal terms Z and χ were evaluated at T = 0 noniteratively with initial value
Z = 1 and χ = 0 and without Wph , whichcorresponds to the G0 W0 approximation. The
equation for the nondiagonal term φ was laterlinearized with Wph and its largest eigenvalue,
which becomes unity at the transition temperature, was calculated. The iterative method

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

was used, which required only no more thana few hours in a serial run at system B at
T ∼ 100K.

with Z
with Z and χ

3.5

1 − ∆ (1/m∗ )Z[1 − ∆ (1/m∗ )Z ] [1 − ∆ (1/m∗ )χ ]

Λ0/Λ

3

References[1] G. M. Eliashberg, Zh. Eksp. Teor. Fiz. 38,(1960) 966.

2.5
2[2] A. B. Migdal, Zh. Eksp. Teor. Fiz. 34,(1958) 1438.

1.5
1using the HSE06 exchange-correlation functional [11] and spin-orbit coupling required
half a day per run with 2 nodes in fat groupin Ohtaka. For the memory requirement thejobs could not run in cpu queue group. Theband structure was interpolated using Wannier90 [12].

0

1

2

3
rs

4

5

6

[3] P. Morel and P. W. Anderson, Phys. Rev.
125, (1962) 1263.
[4] Y. Takada, J. Phys. Soc. Jpn. 45 (1978)
786.

Figure 1: Ratio of the largest eigenvalues of thelinearized Eliashberg equation with and without the self-energy corrections. Lines indicate
approximate formulas. Taken from Ref. [7]We show the results of the corrections to thepairing strength in Fig. 1. The blue open (purple closed) squares denote the inverse ratio of
the largest eigenvalues with Z (Z and χ) to
the non-interacting value. The values larger
than unity indicate suppression of the pairingby the Coulomb self energy. The χ term has
been known to cancel the effect of Z for theeffective mass. In contrast, our result showsthat χ cooperate with Z for the pairing suppression. This result highlight the importanceof χ for superconducting calculations, whichis computationally demanding and has mostly
been ignored in the previous studies.
We also analyzed the band structure of acompound HgCr2Se4 [8]. The calculations
were performed using Quantum Espresso [9]with norm conserving pseudopotentials from
Pseudo dojo [10]. Self consistent calculations[5] R. Akashi and R. Arita, Phys. Rev. Lett.
111 (2013) 057006.
[6] A. Davydov, A. Sanna, C. Pellegrini, J. K.
Dewhurst, S. Sharma, and E. K. U. Gross,
Phys. Rev. B 102, (2020) 214508.
[7] R. Akashi, Phys. Rev. B 105 (2022)
104510.
[8] H. Tanaka et al., Phys. Rev. Lett. accepted.
[9] P. Giannozzi et al., J. Phys. Condens.
Matter 29, (2017) 465901.
[10] G. Pizzi et al., J. Phys. Condens. Matter
32, (2020) 165902.
[11] J. Heyd, G. E. Scuseria, and M. Ernzerhof, J. Chem. Phys. 436 124, (2006)
219906.
[12] N. Marzari and D. Vanderbilt, Phys. Rev.
B 56 (1997) 12847.

313

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Gapless symmetry-protected topological phase ofquantum antiferromagnets on anisotropic triangular
strip
Yuichiro HIDAKA1 , Shunsuke C. FURUYA2 , Atsushi UEDA1 , and Yasuhiro TADA3
1
Institute for Solid State Physics, University of Tokyo
Kashiwa-no-ha, Kashiwa, Chiba 277-8581
2
Department of Basic Science, University of Tokyo
Meguro, Tokyo 153-8902
3
Quantum Matter Program, Graduate School of Advanced Science and Engineering,
Hiroshima University, Higashihiroshima, Hiroshima 739-8530

Classifying phases of matter is one of theparamount issues in condensed matter physics.
One of the important classes of the quantum phase of matter is a symmetry-protected
topological (SPT) phase. An SPT phase is a
gapped phase that preserves some given symmetries but is not adiabatically connected toa trivial phase without breaking the symmetries [1]. Recently many researchers attempt
to extend the concept of the SPT phase into
gapless systems [2, 3].
One of the gapless SPT phases we study isthe anisotropic triangular strip (ATS) model,
which is the three-leg XXZ spin ladder withthe frustrated XXZ rung interaction. The lattice is shown in Fig.1. According to the ﬁeldtheoretic analysis, if the rung coupling is sufﬁciently small, the ground state of the ATSmodel is the gapless SPT state, in that the
ground state has simultaneously the properties of the Haldane state and the TomonagaLuttinger liquid. This property is also supported by the numerical calculation [4].
In the present study, we investigate theground state of the ATS model, where we expand the region of the parameters. We used
the ITensor and TeNPy libraries [5, 6]. We
found that the entanglement spectrum of theATS model is doubly degenerate, which is theimportant feature of the Haldane state if therung coupling J× is suﬃciently small (Fig.2).
Moreover, from the calculation of the entanglement entropy, we found that the ground state
has the central charge c = 1. On the other
hand, if the rung coupling J× is suﬃciently
larger than the ladder coupling J, the groundstate becomes the ferrimagnetic state. This ischecked by the calculation of magnetization.
Finally, if the rung coupling J× is of the sameorder as the ladder coupling, the ground stateis in the trivial phase, in which we cannot see
the even-fold degeneracy of the entanglement
spectrum, and is in a gapless phase. The phase
diagram of the ATS model is determined asshown in Fig.3 [7].

Figure 1: The anisotropic triangular strip(ATS) XXZ model. J denotes the ladder coupling and J× denotes the frustrated rung coupling. All coupling is the XXZ interaction:
Six Sjx + Siy Sjy + ∆Siz Sjz .

314

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo[7] Y. Hidaka, S. C. Furuya, A. Ueda, and Y.
Tada, in preparation.

Figure 2: The entanglement spectrum of theATS model with ∆ = 0.8. When J× < 0.54 we
can see the even-fold degeneracy of the entanglement spectrum, which implies the Haldane
state.

Figure 3: The phase diagram of the ATSmodel.

References[1] X. Chen, Z.-C. Gu, and X.-G. Wen, Phys.
Rev. B 83, 035107 (2011).
[2] T. Scaﬃdi, D. E. Parker, and R. Vasseur,
Phys. Rev. X 7, 041048 (2017).
[3] S. C. Furuya and M. Oshikawa, Phys.
Rev. Lett. 118, 021601(2017).
[4] Y. Hidaka, S. C. Furuya, A. Ueda, and Y.
Tada, Phys. Rev. B 106, 144436(2022).
[5] M. Fishman, S. R. White, and E. M.
Stoudenmire, arXiv:2007.14 (2020).
[6] J. Hauschild and F. Pollmann, SciPost
Phys. Lect. Notes , 5 (2018).

315

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Finite temperature calculations of frustrated
quantum spin systems
Katsuhiro MORITA
Department of Physics and Astronomy, Faculty of Science and Technology,
Tokyo University of Science, Noda, Chiba 278-8510

The S = 1/2 kagome antiferromagnets are
known to have exotic properties even at ﬁnite temperatures. The speciﬁc heat has amultiple-peak structure [1], and the 1/3 magnetization plateau is expected to melt asymmetrically at ﬁnite temperatures [2, 3]. These
properties do not appear in the triangular lattice model, for example. Therefore, theseproperties are considered to be unique to the
kagome lattice model.
In this study, we have calculated the magnetic speciﬁc heat, entropy, magnetic susceptibility, and ﬁnite temperature magnetization
curves for the S = 1/2 J1 − J2 kagome lattice
Heisenberg model using the orthogonalized ﬁnite temperature Lanczos method [4, 5] on Supercomputer System B at the ISSP. J1 represents the nearest-neighbor interaction and J2
represents the next nearest-neighbor interaction. Here we set J1 = 1 as the energy unit.
In this study, the calculations were performed
with a 36-site kagome cluster.
We have found that the entropy remains
at low for −0.06 < J2 < 0.02, and outside
this range, the entropy decreases rapidly. At
J2 ≃ 0, the magnetic speciﬁc heat has a triplepeak structure, but as |J2 | increases, the speciﬁc heat tends to have a double-peak structure. In the ﬁnite temperature magnetization
curves, there is asymmetric melting of the 1/3
magnetization plateau around J2 = 0, but the
1/3 plateau becomes clearly visible as |J2 | increases. This is because the q = 0, up-up√√
down state stabilizes for J2 > 0, the 3 × 3

up-up-down state stabilizes for J2 < 0, and theplateau becomes unstable around the boundary.
These results will be compared with experimental results in the future to clarify the magnetic properties of the compounds.

References[1] T. Shimokawa and H. Kawamura, J. Phys.
Soc. Jpn. 85, 113702 (2016).
[2] J. Schnack, J. Schulenburg, and J.
Richter: Phys. Rev. B 98, 094423 (2018).
[3] T. Misawa, Y. Motoyama, and Y. Yamaji:
Phys. Rev. B 102, 094419 (2020).
[4] K. Morita and T. Tohyama: Phys. Rev.
Research 2, 013205 (2020).
[5] K. Morita: Phys. Rev. B 105, 064428(2022).

316

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Machine learning molecular dynamics studies of complex
structural materials
Masahiko OKUMURA1, Hayato SHIBA2, Masatoshi HANAI3, Yasunobu ANDO4, Akiko
YAMAGUCHI1, Keita KOBAYASHI1, and Hiroki NAKAMURA1
1

Center for Computational Science and e-Systems, Japan Atomic Energy Agency,
Wakashiba, Kashiwa, Chiba 277-0871
2

Information Technology Center, University of Tokyo, Chiba 277-0882
3

Graduate School of Information Science and Technology,
University of Tokyo, Tokyo 133-8658

4

Research Center for Computational Design of Advanced Functional Materials,

National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki 3058568
dynamics

other methods. To overcome this weak point, the(MLMD) is a promising simulation method withmulti-stream EKF method is often used withhigh accuracy and low computational costs [1].

parallel computers [2]. The MD simulations

Artificial neural networks (ANNs) are used towith the ANN potentials for large systems are

reproduce

usually conducted on a supercomputer.

Machinelearning

theresults

molecularoffirst-principles

calculations (FPCs). The ANNs are trained using

Preparing the training dataset and training

datasets that consist of a lot of the results of

the ANNs are the time-consuming steps of

FPCs. For this method, supercomputers are used

MLMD. Notably, the ANNs for complex

for 1) making huge amounts of the FPC results,

materials require a large dataset, because many2) training the ANNs, and 3) conducting

atom configurations are realized, where themolecular dynamics (MD) simulations with thecomplex materials are, in this report, defined as

ANN potentials. Density functional calculations

those with large unit cells with periodic

are popular for making the training data. Several

boundary conditions. For making a dataset of

methods are used to train the ANNs, e.g.,complex materials, a large number of FPCs withstochastic gradient descent, adaptive momentum

many atom configurations in large unit cells are

estimation, extended Kalman filter (EKF), etc.

required. With the large dataset, heavy ANN

The EKF method is known as one of the best

training

optimizers for the ANN potential. However, thecalculations are popular to make the data, whichcalculation speed for an epoch is slower than the

are

317

is

popularneeded.

Densitycalculationsonfunctional
the

ISSP

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

training

Figure 1 shows the relation between thecalculations may not be popular. Therefore, we

number of cores and epoch/time (1/s) for Systemevaluated the scaling of the ANN training on

B and Oakbridge-cs. The blue and orange plots

System B, and the results are compared withshow the results of Oakbridge-cx, where thethose given by Oakbridge-cx operated by theformer and the latter are the results ofInformation Technology Center, The University

calculations using all and half of the cores on aof Tokyo. We prepared 7,007 data of ThO2,

CPU, respectively. We found that the reduction

which shows superionic transition below itsof the active cores improves the performance.

melting point [3]. Therefore, large unit cells areThis may be because the relative memory size

needed to reproduce the superionic transition

was increased by the reduction of the active

and melt, i.e., ThO2 can be considered a complex

cores. The gray and yellow plots represent thematerial although the structure of its solid state

results of System B, where the former and the

is

the

latter are the results of calculations using all andconfigurations of solid, super-ionic, and liquidhalf of the cores on a CPU, respectively. For

states of ThO2. The n2p2 open-source software

System B, the same tendency was obtained, i.e.,

for MLMD [4] was used, which provides thethe reduction of the active cores accelerates themultistream EKF method using MPI.

learning speed.

supercomputer.

simple.

But

The

the

ANN

datasetcontains

epoch/time (1/s)

We investigated the relation between the
0.006

number of cores and the number of epochs per

0.005

second. We found that the reduction of active

0.004

cores improves the calculation speed both for
System B and Oakbridge-cx.

Oakbridge_1

0.003

Oakbridge_2
systemB_1

0.002

systemB_2

References

0.001[1] J. Behler and M. Parrinello, Phys. Rev. Lett.

0
0

200

400

600

800

1000

1200

cores

98, 146401 (2007).

Fig. 1 The relation between the number of cores[2] A. Singraber et al., J. Chem. Theo. Comp. 15,

and the number of steps per second is shown.

3075 (2019).

The blue and orange plots represent full- and[3] K. Kobayashi et al., Sci. Rep. 12, 9808

half-core(2022).

calculations

on

Oakbridge-cs,

respectively. The gray and yellow pots represent[4] A. Singraber et al., J. Chem. Theo. Comp. 15,

full- and half-core calculations on Oakbridge-cs,

1827 (2019).

respectively.

318

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Multi-impurity method for bond-weighted tensor
renormalization group
Satoshi MORITA
Graduate School of Science and Technology, Keio University
Kohoku-ku, Yokohama, Kanagawa 223-8522

In this study, we propose an algorithm
to calculate higher-order moments of physical quantities based on BWTRG. We introduce a bond-weight impurity which representsa physical quantity. With the appropriate update rules, coarse-grained bond-weights containing multiple impurities can be generated
to calculate the higher-order moments of thephysical quantity. Our proposed method iscompared with conventional methods on thetwo-dimensional classical spin model. The
proposed method achieves higher accuracy atlower computational cost than the higher-order
TRG algorithm [4]. We also show that thefinite-size scaling analysis of the squared magnetization provides critical exponents and distinguishes the weakly first-order and continuous phase transitions.

0.5

TRG
BWTRG
Exact

0.4
0.3

m2

Tensor network (TN) methods are attracting
much attention as powerful tools for computing strongly correlated many-body problems.
The partition function of classical statisticalsystems can be represented by the TN form.
However, the contraction of a large TN stillrequires an exponentially large computational
effort. The concept of the real-space renormalization group resolves this problem. The tensor renormalization group (TRG) method andits variants calculate a coarse-grained tensor
by information compression using the singularvalue decomposition [1, 2]. These methods cancalculate the partition function approximately
in logarithmic time for the system size. Recently, Adachi, et al. have proposed the bondweighted TRG (BWTRG) method, which improves the accuracy of TRG by introducing
a bond weight and distributing it appropriately [3].

0.2
0.1
0.0
2.24

2.25

2.26

2.27

T

2.28

2.29

2.30

Figure 1: The squared magnetization of theIsing model on the square lattice calculated by
TRG and BWTRG with the bond dimension
χ = 128 and the system size L = 225 . The
green solid curve shows the exact solution in
the thermodynamic limit.

References[1] M. Levin and C. P. Nave, Phys. Rev. Lett. 99,
120601 (2007).
[2] Z. Y. Xie, J. Chen, M. P. Qin, J. W. Zhu, L.
P. Yang, and T. Xiang, Phys. Rev. B 86, 1(2012).
[3] D. Adachi, T. Okubo, and S. Todo, Phys. Rev.
B 105, L060402 (2022).
[4] S. Morita and N. Kawashima, Comput. Phys.
Commun. 236, 65 (2019).

319

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Majorana Kramers Qubits and Yang-Lee Anyons in
Topological Superconductors
Takeshi MIZUSHIMA
Department of Materials Engineering Science, Osaka University
Toyonaka, Osaka 560-8531

We have investigated non-Abelian statisticsof Majorana Kramers pairs (MKPs) in timereversal invariant topological superconductors(TRITSCs) [1].
Numerically solving thetime-dependent Bogoliubov-de Gennes equation, we have simulated the braiding dynamics of MKPs in a network of one-dimensional
TRITSCs. We have first considered the tolerance of MKPs against a perturbation with broken time-reversal symmetry such as a magnetic
field and demonstrated the robustness of the
non-Abelian braiding of MKPs when the initial and final states of a braiding process maintain the combined symmetry of time reversal
and mirror reflection symmetries. The Majorana Kramers qubit is tolerant even when intermediate states break the combined symmetry. We have also discussed the eﬀect of gateinduced inhomogeneous potentials at junctions
between superconducting nanowires. Such a
potential generally induces a non-Majorana
nearly zero-energy Andreev bound state at thejunctions. Our numerical simulation showsthat the dynamics of MKPs accumulate significant errors when such non-Majorana states exist and interfere with MKPs. Although we concentrated on Majorana particles in a TRITSC
in this work, it is expected that eﬀects of nonMajorana states generated by inhomogeneousgate potentials on the non-Abelian braiding
are ubiquitous for any topological superconductors with broken time-reversal symmetry.
We have also demonstrated that a Yang-Leeanyon system is constructed from collective

Majorana bound states in a network of topological superconducting nanowires [2]. YangLee anyons are nonunitary counterparts of Fibonacci anyons, obeying the same fusion rule.
Because of nonunitarity, the central charge andthe scaling dimension for the one nontrivial
primary field are negative, c = −22/5 and∆ = −2/5, respectively. The nonunitary conformal field theory with c = −22/5 describes
the nonunitary critical phenomenon known as
the Yang-Lee edge singularity. In Ref. [2],we consider a topological superconductor junction system coupled with dissipative electron
baths as a non-hermitian platform for interacting Majorana quasiparticles. Numericallycalculating the central charge and scaling dimension, we find the condition that the nonHermitian Majorana system can simulate theIsing spin model of the Yang-Lee edge singularity and confirmed that, by controlling model
parameters in a feasible way, the Yang-Leeedge criticality is realized. We also discuss thescheme for the measurement and the braidingof Yang-Lee anyons, aiming at the application
to universal quantum computation.

References[1] Y. Tanaka, T. Sanno, T. Mizushima, andS. Fujimoto: Phys. Rev. B 106, 014522(2022).
[2] T. Sanno, M. G. Yamada, T. Mizushima,and S. Fujimoto: Phys. Rev. B 106,174517 (2022).

320

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Quantum Monte Carlo simulations of
Spin Hall magnetoresistance in quasi-two-dimensional
antiferromagnetic insulator/metal bilayer systems
Takeo Kato
Institute for Solid State Physics,
The University of Tokyo, Kashiwa-no-ha, Kashiwa, Chiba 277-8581
Spin Hall magnetoresistance (SMR) is a
novel type of magnetoresistance caused by spincurrent. SMR was firstly observed in 2013 by
Nakayama in a bilayer system composed of ametal and a magnetic insulator [1]. The
phenomenological theory for SMR [2] is widelyused for analysis of SMR, but it can’t describe
the temperature dependence of SMR in a bilayer
systemcomposed

of

a

metal

and

Fig. 1:Setup for SMR calculation cited from[5].

anWe used DSQSS for the quantum Monteantiferromagnetic-insulator [3] .

In this study, we discuss the SMR

Carlo method, and we adjustment some code

dependence of temperature, thickness of AFI,

to be able to calculate the local spin

and randomness of exchange interaction. To

susceptibility. The accuracy for the calculation

discuss about these features, we expand theis proportional to the number of Monte Carlomicroscopic theory of SMR based on Keldysh

steps,

Greens function [4]. In this theory the spin

calculations with MPI, which is included incurrent is expressed in terms of the spin

DSQSS, to achieve a high degree of accuracy.

susceptibilityof

antiferromagnetic

We show the result of this calculations

insulator, but analytical calculation based on

based on the paper [5]. The temperature andspin-wave theory are available for only low

thickness dependence of SMR is shown in Fig.

temperature.

widely2. The black points indicate the total SMR

temperature dependence of SMR by thesignal, whereas the red and blue plots arequantum Monte Carlo method, we obtained

contribution from z-component and xy-

thecomponents of spin susceptibility. Green

Due

the

so We performed parallelizationtocalculate

spin conductance formulation by

Matsubara Greens function [5].

arrows indicate the ordering temperature.

321

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

The effect of randomness by disorderedexchange interactions is shown in Fig. 3. It
shows that the ordering temperature issuppressed and the SMR signal becomes small
in the disordered case.
We calculated SMR in AFI by quantum Monte
Carlo simulations, and we discussed about the
SMR dependence of temperature, thickness ofFig. 2: S=1 result for SMR cited from [5].

AFI, and randomness of exchange interaction.

It shows that at high temperature SMR
signals vanish in consistent with the analytical
calculation by the high temperature expansion.
SMR has a peak less than the ordering

References

temperature and the thickness dependence[1] H. Nakayama, et al., Phys. Rev. Lett.

becomes weaker for four or more layers.

110, 206601 (2013).
[2] Y. T. Chen, et al., Phys. Rev. B 87,
144411 (2013).
[3] D. Hou, et al., Phys. Rev. Lett. 118,
147202 (2017).
[4] T. Kato, Y. Ohnuma, and M. Matsuo,
Phys. Rev. B 102, 094437 (2020).
[5] T. Ishikawa, M. Matsuo, and T. Kato, Phys.
Rev. B 107, 054426 (2023).

Fig. 3: Disordered case of result cited from [5].

322Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Precise structural analysis of atomic arrangement ofhydrogen-adsorbed ceria surfaces by using 2DMAT
Izumi MOCHIZUKI
Slow Positron Facility, Institute of Materials Structure Science, High Energy Accelerator
Research Organization (KEK), Oho 1-1, Tsukuba, Ibaraki, 305-0801, Japan
We used 2DMAT [1-7], an open-source data-as an innovative feature of TRHEPD. Moreanalysis software that is preinstalled on the ISSP

detailed analysis is ongoing.

supercomputers, developed by the PASUMS

References

project at FY2020 and 2021 for data analyses of[1]

total-reflection high-energy positron diffraction

https://www.pasums.issp.utokyo.ac.jp/2DMAT(TRHEPD). TRHEPD is an innovative method[2] K. Tanaka, et al. (I. Mochizuki), Acta. Phys.

forPol. A 137, 188 (2020).

theprecise determination of atomic

arrangement of crystal surfaces due to its[3] T. Hoshi, et al. (I. Mochizuki), Comp. Phys.

exceeding surface sensitivity. It is realized atCommun. 271, 108186 (2022).

Slow Positron Facility, IMSS, KEK [8].

[4] T. Hanada, et al., Comp. Phys. Commun. 277

We applied 2DMAT to the structural analysis

108371 (2022).

of a Ge(001)-c(4×2) surface as a test of the[5] Y. Motoyama, et al. (I. Mochizuki), Comp.

analysis method by an optimization procedure,Phys. Commun. 280, 108465 (2022).

Nelder-Method method [5, 7].

[6] K. Tanaka, et al. (I. Mochizuki), JJAP Conf.

We also investigated a hydrogen (H)Series 9, 011301 (2023).

adsorbed CeO2(111)-(1×1) surface [9]. It was a[7] N. Kinoshita, et al. (I. Mochizuki), in JPS

challenge for the detection of surface hydrogenmeeting, 22-25, Mar. 2023.

since it is quite difficult for other experimental[8] https://www2.kek.jp/imss/spf/eng/

measurements. In this study, we first calculated[9] I. Mochizuki, in Innovation catalysis science

the structure of the bare CeO2(111)-(1×1)

through the integration of high-performancesurface and that of the H-adsorbed one by using

data-driven science and advanced measurement

Quantum ESPRESSO in the DFT+U method.

technology,

The results were then compared with those

Usage/Research Center for Catalysis (JURCC),

obtained by the TRHEPD experiment. We found30.

that the TRHEPD can detect the surface

https://sites.google.com/view/hpc-catal2023

hydrogen properly with the data-driven analysis,323

onlinemini-symposium,
Mar.

Joint
2023,

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Equilibration and glass transition in the dense hard
sphere systems
Masaharu ISOBE
Nagoya Institute of Technology
Gokiso-cho, Showa-ku, Nagoya, 466-8555

As the simplest models, the hard
disk/sphere systems have been investigated
via molecular simulation in both equilibriumand non-equilibrium phenomena [1].
Inthis project, we focus on the equilibrationand the non-equilibrium phase transitionin the hard disk/sphere model system withmodern algorithms, i.e., Event-Chain Monte
Carlo (ECMC) [2, 3], Newtonian ECMC
(NEC) [4] and Event-Driven Molecular
Dynamics(EDMD) [5].
Eﬃciency of Newtonian Event-Chain Monte
Carlo in dense hard disk systems
In the statistical mechanics of many bodysystems, the physical properties at the true
equilibrium state are crucial. To invent efficient method of equilibration in the molecular simulation is one of fascinating topics
where the simple model systems such as hardspheres and Ising spins have been investigatedactively and made important contribution. In
2009, Event-Chain Monte Carlo (ECMC) [2]
was invented in hard disk systems that updates the particle position with sequential collisions (i.e., event-chain) and has been applied
to the many typical models studied in statistical physics [3]. ECMC has several variants depending on how the direction of movement of the particles after the collision is determined. Recently, Newtonian-ECMC (NEC) [4]
was invented by applying the velocity collision rule to each collision event to dictate thedirection of particle movement; this is often
used in the Event-Driven Molecular Dynamics(EDMD) of hard-sphere systems. NEC outperformed not only other variants of ECMC but
EDMD in the diﬀusion coeﬃcient and melting
process in hard sphere systems. However, theoptimal performance strongly depends on thephysical properties and chain length (or duration) of ECMC (and its variants). Previously,
we found the high eﬃciency of NEC in diﬀusion coeﬃcient of liquid state in hard disk systems for all chain lengths and various systemsizes, which is caused by the spatially homogeneous sampling of the displacement (i.e., collision) [7]. To figure out the mechanism of equilibration of NEC in dense systems, we focus on
the equilibrium state of dense hard disk systems above the fluid-to-solid transition point,
which show the relaxation via hopping motion
even in the packing fraction at the solid phasein hard disk systems for each methods. We also
found profoundly eﬃcient diﬀusion coeﬃcientby NEC even when the relaxation becomes
only successive hopping motion in highly dense
systems.
Free volume estimator (NELF-A) in dense
poly-disperse hard disk systems — local structure analysis on pressure and inherent structure
In many-particle systems, the free volume
of a tagged particle constructed from excluded
volume by surrounding particles made a crucial contribution to describing the macroscopic
properties in the history of liquid state theory.
Several numerical algorithms for calculating
the free volume (FV) have been invented, such

324

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

as Monte Carlo (MC) sampling and Voronoi
tessellation. However, MC is an approximate
calculation, and the Voronoi tessellation requires high computational costs (especially forpoly-disperse systems). As an alternative algorithm, we propose the simple, eﬃcient, andprecise method, Neighbors for Enclosing Local
Free Area (called NELF-A), which is easily applied to dense poly-disperse hard disk systems
often used in glassy model systems [8].
As an application of the NELF-A, we implement an alternative method to obtain the inherent structure instead of short-time averaged
coarse-graining trajectories. The summary of
the algorithm is as follows: (i) The displacement vectors from a particle position to a geometric center of FV estimated by NELF-A foreach particle are drawn. (ii) The positions in
a whole system are updated with the displacement vectors multiplied by a scaling parameterβ less than unity. (iii) Repeat (ii) until the positions converge toward an inherent structure.
The FV in equilibrium state eventually converging positions after (i)-(iii) procedure andthe isotropic shape of FV located on a triangular lattice could be observed (i.e., inherent
structure). Even in a typical non-equimolar
binary mixture hard disk system, the probability density distribution obtained by (i)-(iii)
procedure is fairly consistent with the conventional method based on the short-time averaged coarse-graining trajectories. We also confirmed the accuracy of this method by estimating pressure based on FV-based as comparedwith the other conventional methods.

tional order (so-called Alder transition) due tothe exclusive eﬀect of hard disk, which causes
anomalous fluctuations around phase transition and transition shifts [9].

References[1] M. Isobe: Mol. Sim. 42 (2016) 1317.
[2] E. P. Bernard, W. Krauth, and D. B. Wilson: Phys. Rev. E 80 (2009) 056704.
[3] W. Krauth: Front. Phys. 9 (2021) 229.
[4] M. Klement and M. Engel: J. Chem.
Phys. 150 (2019) 174108.
[5] M. Isobe: Int. J. Mod. Phys. C 10 (1999)
1281.
[6] M. Klement, S. Lee, J. A. Anderson and
M. Engel: J. Chem. Theor. and Comp. 17(2021) 4686.
[7] H. Banno, D. Mugita, and M. Isobe: J. of
Phys.: Conf. Ser. 2207 (2022) 012011.
[8] K. Souno, H. Koyama, D. Mugita, and M.
Isobe: in preparation.
[9] N. Murase and M. Isobe: Proceedingsof the 28th Symposium on Traﬃc Flowand Self-driven Particles, 28 (2023) 9. (inJapanese)

Anomalous phase transition in self-propelled
hard disk systems
In this study, we investigate the phase behavior of the self-propelled hard disk systems
with the Vecsek-type interaction via EDMD
simulation systematically. In addition to theordinal order-disorder transition of the collective velocity field known in the original point
particle of the Vicsek model, we observed thenovel competition driven by the global posi-

325

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Molecular dynamics analyses of ion migration at electrodeelectrolyte interfaces and grain boundaries in electrolytes
Ryo KOBAYASHI
Department of Applied Physics, Nagoya Institute of Technology,
Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555
aresimulation and local ion-flux analyses, weconsidered as a promising candidate for next-

showed that, in poly-crystalline materials, thegeneration lithium-ion batteries. They use aions migrate towards the lower reaches of grainsolid-state electrolyte instead of the liquid-stateand go through spots at GBs, and the high-flux

electrolyte used in conventional lithium-ion

spots contain migration paths as low energy asbatteries.

the bulk ones.

All-solid-stateThebatteries

key

to(ASSBs)achievinghighperformance in ASSBs is to reduce or control

This methodology can be applicable to a

the resistivity at interfaces such as thewide variety of systems for electrolytes andelectrode/electrolyte

interfaces between electrode and electrolyte.

interface

and

grain

boundaries (GBs) within the electrolyte. To
achieve this, we need a deeper understanding of
the atomic structure and precise mechanisms ofion migration at GBs. The goal of this project is

References

to obtain new insights into these processes by[1] R. Kobayashi, Y. Miyaji, Nakano, K. M.

using large-scale molecular dynamics (MD)Nakayama, APL Materials 8, 081111 (2020)

simulations.

[2] Nagoya Atomistic-simulation Package (nap)

In order to obtain atomistic insights on Liion

migrationmechanism

at

GBs,

http://github.com/ryokbys/nap

we[3] Optzer, http://github.com/ryokbys/optzer

performed non-equilibrium MD simulation of[4] R. Kobayashi, K. Nakano, M. Nakayama,

large-scale poly-crystalline Li2Zr(PO4)3 (LZP)

Acta Materialia, 226 (2022) 117596.

system containing half a million atoms using a[5] M. Nakayama, K. Nakano, M. Harada, N.

interatomic potential generated using a meta-

Tanibata, H. Takeda, Y. Noda, R. Kobayashi, M.

heuristic approach [1,2,3] and extracted local

Karasuyama, I. Takeuchi, M. Kotobuki, Chem.

ion flux information around GBs [4]. From theCommun.

326

58,

9328–9340(2022)

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Theoretical study for firefly bioluminescence
substrate analogs and related molecules
Miyabi HIYAMA
Graduate School of Science and Technology, Gunma University
Tenjin-cho, Kiryu, Gunma 376-8515

In this year, we studied the stable structuresof adenosine triphosphate (ATP)-metal complexes in aqueous solutions and the theoreticalabsorption spectra for the firefly luciferin analog named ”seMpai”.
It is believed that the cofactor ATP and
Mg2+ form a complex and react with thesubstrate luciferin in firefly bioluminescence.
Wang et al. [1] reported that the bioluminescence quantum yields decreased with increasing cadmium or zinc concentration whenthese metal ions were used instead of magnesium one. The effects of metals on luciferin,
on ATP, and on proteins may be responsible
for this feature. We expected that the effect of metals on ATP was most significant
than the others because the ATP is directly
related to the bioluminescence reaction. Thus,
the optimized structures of ATP-Mg2+ , ATPZn2+ , and ATP-Cd2+ in aqueous solutions
were obtained using the density functional theory (DFT). For the initial structures for thesecalculations, the results from Global Reaction
Mapping (GRRM11) calculations [2] were usedbecause there were many local minimum structures of these complexes. We found that there
was no clear difference in the structure of these
metal ion coordination to ATP. It also foundthat ATP-Zn2+ and ATP-Cd2+ were unstable
in aqueous solutions, unlike ATP-Mg2+ .
In these days, one of the firefly luciferin
analogs named ”seMpai”, which is preparedwith the aim of improving water solubility
and produces red luminescence with firefly lu-

ciferase, was reported [3]. To elucidate theabsorption characteristics of this analog, theoptimization structure of its ground state forits conjugate acids and bases expected to be
main component in the aqueous solutions wereobtained from DFT calculations. The time dependent DFT (TDDFT) calculations were carried out to estimate the theoretical absorption
spectra for these structures.
The computational chemistry software
package, Gaussian09 [4], was used for DFT
and TDDFT calculations. All calculations using Gaussian09 and GRRM11 were performedon system B of Super Computer Center in
ISSP.

References[1] Y. Wang, H. Kubota, N. Yamada, T. Irie,
H. Akiyama: Photochem. Photobiol. 87,
846 (2011).
[2] K. Ohno, S. Maeda: Chem. Phys. Lett.
384, 277 (2023). S. Maeda, K. Ohno,:
J. Phys. Chem. A, 109 5742 (2005). K.
Ohno, S. Maeda, J. Phys. Chem. A, 1108933 (2006).
[3] R. Saito, T. Kuchimaru, S. Higashi, S. W.
Lu,M. Kiyama, S. Iwano, R. Obata, T.
Hirano, S. Kizaka-Kondoh, S. A. Maki:
Bull. Chem. Soc. Jpn. 92, 608 (2019).
[4] Gaussian09 Revision D.01, M. J. Frisch et
al.

327

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Analysis of Ising model in statistical-mechanical informatics
Yuya SEKI
Graduate School of Science and Technology, Keio University, Kanagawa 223-8522
We have developed an optimization method

use multithreading to reduce computational

for discrete black-box optimization problems

time for the Monte Carlo simulation to

by combining machine learning, simulated

minimizes the model equation of the trained

annealing, and integer encoding methods [1].

FM. By using the parallel computing on the

Our approach is based on factorization machinesupercomputer, we obtained numerical results

withefficiently.

annealing

(FMA)

[2]

that

uses

This year we investigated the performancefactorization machines as machine learningmodel. Factorization machine with annealing

of

FMA

approximates an objective function of a black-

optimization problems in detail [1]. Integer

box optimization problem by training avariables can be represented as binary variables

factorization machine. Instead of minimizing

through integer encoding methods such as

the objective function that requires high cost to

binaryevaluate it, FMA minimizes a model equation

domain-wall encoding. Since the encoded bitof FM. Here, simulated annealing can be usedstrings differ depending on the encoding

for the minimization because the model

methods,

equation of FM has a QUBO form that isdetermined by the choice of the encoding

equivalent to Ising model. Since FM is anmethod. Through a numerical simulation, we

important factor in performance of FMA, wefound that the FMA with one-hot encoding

analyze properties of model equations of FM,

method has higher performance than FMA withwhich are Ising models, by using numericalother encoding methods. By using simulation

simulations on the supercomputer.

on

the

for

integer-variable

encoding,

the

one-hot

performancesupercomputer,

weblack-box

encoding,ofFMA

revealed

required to achive a high performance. To thisthe performance of FMA.

end, we performed the calculation in parallel by

Referenceschanging[1] Y. Seki et al.: arXiv:2209.01016 (2022).

parameters

with

is

the

relationship between sparsity of bit string and

Tuning parameters in the FMA method is

the

and

multiple

processes on the supercomputer. In addition to[2] K. Kitai et al.: Phys. Rev. Research 2

the parallel computing mentioned above, we(2020) 013319.

328

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Quantum Pyrochrole Magnet
H. Kadowaki
Department of Physics, Tokyo Metropolitan University
Hachioji, Tokyo 192-0397, JapanWe are investigating static correlaz σ z ⟩ and dynamic correlations
tions ⟨σQ−Q
z (0)σ z (t)⟩ characterized by the modu⟨σQ−Qlation wave vector k ∼ ( 12 , 12 , 12 ), which wereobserved by neutron scattering experiments,
e.g. Ref. [1, 2], in the frustrated pyrochlore
magnet Tb2+x Ti2−x O7+y (TTO). TTO withx < xc ≃ −0.0025 is very interesting becauseof their quantum spin liquid (QSL) groundstate. A simple question “why TTO shows thestatic short-range order with k ∼ ( 12 , 21 , 12 )?”has been a diﬃcult problem for many years[3]. Recently we have solved this problem
using quantum simulation based on thermal
pure quantum (TPQ) states, and proposedthat small three-spin interaction terms of aform σr± σrz ′ σrz ′′ are a key ingredient of bothk ∼ ( 12 , 12 , 12 ) and the QSL state of TTO at
low temperatures [4].
We are now extending the quantum simuz σ z ⟩ to inelastic neutron speclation for ⟨σQ−Qz (0)σ z (t)⟩ using a
tra, i.e., simulating ⟨σQ−Q
TPQ method [5]. We have written a simulation program, which now works very well,and are performing time-consuming calculations, which we would like to continue in 2023.

References[1] H. Takatsu et al. Phys. Rev. Lett. 116,
(2016) 217201.
[2] H. Kadowaki et al. J. Phys. Soc. Jpn. 87(2018) 064704.

329[3] H. Kadowaki et al. Phys. Rev. B 99 (2019)014406.
[4] H. Kadowaki, M. Wakita, B. Fåk, J.
Ollivier, and S. Ohira-Kawamura, Phys.
Rev. B 105 (2022) 014439.
[5] H. Endo et al. Phys. Rev. Lett. 121 (2018)
220601.

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Atomistic model study on the coercivity mechanism of
permanent magnets
Hiroshi Hayasaka1, Masamichi Nishino2, 1, and Seiji Miyashita3,4,1
1

Elements Strategy Initiative Center for Magnetic Materials, National Institute for Materials
Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan
2

Research Center for Advanced Measurement and Characterization,

National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan
3

Institute for Solid State Physics, The University of Tokyo,
5-1-5 Kashiwanoha, Kashiwa 277-8581, Japan

4

The Physical Society of Japan, 2-31-22 Yushima, Tokyo 113-0033, Japan

1. Angular dependence of coercivity

2. Coercivity enhancement by dysprosium
substitution into neodymium permanent

We investigated angular dependences ofnucleation and pinning fields in anisotropic

magnets

Heisenberg model. Unlike a deterministic

We investigated the features and mechanisms ofprocess in magnetization reversal at zero

the enhancement of the threshold field fortemperature, reversal is a stochastic process formagnetization reversal in atomistic Nd-Fe-B

barrier crossing at finite temperatures. We

model with Dy substitution. The results showedstudied the thermal fluctuation effect using thethat a change from surface to bulk nucleation

stochastic LLG equation method [1].

occurs when the number of substituted layers

At

zero

temperature,thenormalized

increases and the anisotropy energy of Dy is

nucleation field shows an asymmetric bowl-likeresistantangular dependence, and the value at large

significantly enhances coercivity, especially atangles rapidly increases. On the other hand, thehigh temperatures [3].

pining field shows almost constant dependence

Referencesat θ < 40◦ and increases at larger θ. The thermal[1] M. Nishino, and S. Miyashita, Phys. Rev. B

fluctuation effect reduces the nucleation and91, 134411 (2015)

pinning fields. At higher temperatures, the[2] H. Hayasaka, M. Nishino, and S. Miyashita,

thermal effect makes both fields closer, in which

Phys. Rev. B 105, 224414 (2022)

the surface nucleation at the hard magnet plays[3] M. Nishino, H. Hayasaka, and S. Miyashita,

an important role [2].

Phys. Rev. B 106, 054422 (2022)

330

to

temperature

increase,whichActivity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Large-scale molecular-dynamics simulation of pressure-induced
coordination change in silica glass with ANN potentials
Daisuke WAKABAYASHI
Photon Factory, Institute of Materials Structure Science,High Energy Accelerator Research Organization (KEK), Tsukuba 305-0801
Silica is well known as an archetypal oxide

The test calculations were performed for theand the compression behavior of silica glass andsolidification process of silica melt under highmelt has attracted considerable attention inpressure. The ANN potential was determinedvarious research fields.

Theoretical studies

with the total potential energy, atomic force, andusing molecular-dynamics simulations havepressure data obtained by ab-initio calculations

been actively conducted to obtain information

for a temperature range of 300-5,000 K at 60

on the atomic arrangements and bonding states

GPa with 144 atoms. Using the potential, weof silica glass under high pressure. Since there

made molecular-dynamics calculations on silica

is no long-range order in amorphous materials as

melt at 60 GPa and 4,000 K for a system of about

exists in crystals, calculations on a large number

30,000 atoms.

of atoms are required to reproduce the structural

The use of multiple interatomic potentials

changes. There is a limit to the size of theaveraged together stabilizes the calculations.

system which can be calculated by ab-initio

The initial parameter sets for the fitting were

methods.

randomly generated, and five ANN potentials

In this study, we have fitted the potential with

were determined from the five parameter sets,a machine-learning technique (ANN potential)

respectively. In order to avoid extreme results,

on the basis of the results of ab-initio

molecular-dynamics

calculations, and have applied it for the

performed in an extended system using the

calculations with an extended system. However,average value of the atomic forces calculated byin our previous projects, it has been observed

the five ANN potentials.

simulations

were

that the large-scale calculations of melt wereIn addition, active learning was carried out todifficult to stabilize and quickly diverged,

efficiently compensate for the lack of data. First,

making them impossible to continue. In thismolecular-dynamics

project, potential averaging and active learning

performed at 60 GPa and 4,000 K with theareaveraged ANN potentials in a system of the same

employedincombination

to

avoid

computational instability efficiently [1].

simulations

weresize as in the ab initio calculations. The atomic

331

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

forces calculated from each ANN potential

calculations, but have also been found toshould agree, and therefore it is likely thatsignificantly improve the precision of theatomic configurations at steps having a large

intermediate-range structure and reproduce thevariation in atomic force among the fiveexperimental data in the literature [3].

potentials have not been well learned. Then, thesuggests that the experimentally observed

standard deviation of the atomic forces was

structural transformation of silica glass, in which

estimated for all atoms at each step, and thethe coordination number of Si changes from 4 toatomic configurations for which the maximum

6 with increasing pressure [4], can be simulated

value was larger than 0.25 eV/Å were extracted

in more detail. In fact, the calculations of the[2]. The total potential energies, atomic forces,

depressurization process down to 0 GPa wereand pressure in the extracted configurations

successful with the ANN potentials determined

were calculated by an ab initio method andin the same method as for the cooling process

added to the initial dataset. Active learning was

and reproduced the change in coordination

carried out twice in total.

number from 6 to 4.

This

The averaged ANN potential obtained after
two active-learning cycles did not cause any

Referencescomputational instability during the simulations[1] D. Wakabayashi, K. Shimamura, A. Koura,at 60 GPa and 4,000 K and in the subsequentand F. Shimojo, submitted to J. Phys. Soc. Jpn.

simulations of the cooling process down to 300[2] Y. Zhang, H. Wang, W. Chen, J. Zeng, L.

K.

Zhang, H. Wang, and W. E, Comput. Phys.

Without active learning, the calculation

becomes unstable before 2,000 steps and cannot

Comm. 253, 107206 (2020).

reproduce the results of the ab initio calculations.

[3] D. Wakabayashi, N. Funamori, T. Sato, and

The time at which the destabilization occurs is

T. Taniguchi, Phys. Rev. B 84, 144103 (2011).

significantly delayed with each active-learning[4] T. Sato and N. Funamori, Phys. Rev. B 82,

cycle.

184102 (2010).

The calculations on silica glass at 60 GPa and300 K have not only reproduced ab initio

332

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

First-principles investigation of ductile deformation
mechanism in silver sulfide
Masaaki MISAWA
Department of Intelligent Mechanical Engineering, Fukuoka Institute of Technology
Wajiro-higashi, Higashi-ku, Fukuoka 811-0295

β-Ag2 S, a low-temperature phase of silver
sulfide, exhibits metal-like ductility at room
temperature even though it is an inorganic
semiconductor[1]. Elucidating the origin of
this ductility will be extremely useful for designing materials for flexible next-generation
semiconductor devices. Although several theoretical investigations have been done to elucidate the mechanism of ductile deformation of
β-Ag2 S from the viewpoint of bonding statesin crystals [1, 2], theoretical verification considering dynamics under finite temperatures has
not yet been performed. Therefore we have investigated the behavior of β-Ag2 S under shear
stress based on first-principles molecular dynamics method [3].
In this study, we have performed firstprinciples molecular dynamics simulations forthe six types of deformations of β-Ag2 S crystals: the (100)[010], (100)[001], (010)[100],(010)[001], (001)[100], and (001)[010] shear,
where (KLM )[klm] shear means that simple shear deformation in which the crystal’s(KLM ) plane slides toward the [klm] direction. The shear rate in the simulations was
equivalent to dγ/dt = 0.05 ps−1 , and shear
deformations up to γ = 0.50 were performed
under room temperature (300 K). The radial
distribution function of the sulfur ion sublattice during the deformation process wasanalyzed to elucidate atomistic behaviors of
β-Ag2 S under shear stress, and it was observed that peaks that once disappeared during deformation were restored in the four sys-

tems: (100)[001], (010)[100], (001)[100], and(001)[010] shear. In addition, we performeda more detailed study for the (100)[010] and(010)[001] shear systems, in which structural
recovery did not occur, by slowing the shear
rate, and observed structural recovery in thesesystems as well. A common feature of thesestructural recovery mechanisms is that thefracture is repaired by the mass movement ofsulfur atoms in a particular crystal plane.
We also pointed out that the BCC-like structure of the sulfur sublattice in β-Ag2 S may be
responsible for this atomistic behavior. Ag2 Se,
which is not ductile, does not have such ahighly symmetric anion-sublattice and thus
cannot undergo the mass movement of atoms
in response to shear stress, resulting in difficulty in structural recovery. Such atomistic
understanding is expected to accelerate research and open a possibility in designing awide variety of inorganic ductile semiconductors for future flexible electronics based on silver chalcogenides.

References[1] X. Shi et al.: Nat. Mater. 17, 421 (2018).
[2] G. Li et al.: npj Comput. Mater. 4, 44(2018).
[3] M. Misawa et al.: Sci. Rep. 12, 19458(2022).

333

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Development of machine-learning-based phase
diagram construction method for high-throughput
batch experiments
Ryo TAMURAInternational Center for Materials Nanoarchitectonics,
National Institute for Materials Science,
1-1 Namiki, Tsukuba, Ibaraki, 305-0044

Phase diagrams are valuable guidelines forthe development of new materials. Throughexperiments, phase diagrams have been determined in various spaces, such as between processes, between external ﬁelds, and between
compositions. In the ﬁeld of materials informatics, tools focusing on phase diagrams
have been developed using machine learning(ML). Among these tools, we have developed
an ML method based on uncertainty sampling(US)[1] to eﬃciently construct phase diagrams,
which we call the Phase Diagram Construction(PDC) package[2]. This method evaluates theuncertainty based on the ML model learned
under the conditions that the phase domain
is speciﬁed. Using evaluated uncertainty, theexperimental condition with most uncertainty
in the phase diagram is selected as the next
experimental condition. Based on this suggestion, an experiment will be conducted and thecorresponding phase domain is identiﬁed. The
number of known points is then increased andthe next experimental condition is proposedusing the updated ML model. By applying
the PDC to the known phase diagram, it wasdemonstrated that this iterative process canrapidly construct an accurate phase diagramand discover new phase domains[1]. For further validation, PDC was applied to construct
a new diagram for obtaining Zn-Sn-P ﬁlms bymolecular beam epitaxy[3]. The PDC experiments detected new phases that were not ob-

served in the initial stage, and detailed phaseboundaries were quickly determined. Thus,
the PDC proved to be useful in the case where
one condition is proposed in ML and one experiment is performed in each iteration. Further speed-up can be achieved by incorporating
the knowledges of materials science[4].
In addition, we developed PDC for highthroughput batch experiments[6]. To address
the performance of the method, the target wasa Cu-Mg-Zn ternary alloy. Both isothermal
sections of this system (two-dimensional) andits temperature-dependent phase diagram asa whole (three-dimensional) were considered.
We showed that PDC is suitable for highthroughput batch experiments to eﬃciently
construct phase diagrams. Using this version,a temperature-composition phase diagram ofa crosslinked polymer was constructed, and its
usefulness was conﬁrmed[5].

References[1] K. Terayama, R. Tamura, Y. Nose, H. Hiramatsu, H. Hosono, Y. Okuno, and
K. Tsuda, Physical Review Materials 3,033802 (2019).
[2] https://github.com/tsudalab/PDC[3] R. Katsube, K. Terayama, R. Tamura,and Y. Nose, ACS Materials Letters 2,
571-575 (2020).

334

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo[4] K. Terayama, K. Han, R. Katsube,
I. Ohnuma, T. Abe, Y. Nose, and
R. Tamura, Scripta Materialia 208,
114335 (2022).
[5] W. Hu, T. Chen, R. Tamura, K. Terayama, S. Wang, I. Watanabe, and
M. Naito, Science and Technology of Advanced Materials 23, 66-75 (2022).
[6] R. Tamura, G. Deﬀrennes, K. Han,
T. Abe, H. Morito, Y. Nakamura,
M. Naito, R. Katsube, Y. Nose, andK. Terayama, Science and Technology ofAdvanced Materials: Methods 2, 153-161(2022).

335

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Study on Novel Algorithm for Ising Machines
Shu Tanaka
Department of Applied Physics and Physico-Informatics, Keio University
Hiyoshi, Yokohama, Kanagawa, 223-8522
Ising machines are special computers forproperties of the proposed method were not

combinatorial optimization problems. The Ising

clarified. We have used the method of amachine expresses the objective function andprevious study [2,3] to study the slow

constraints of a combinatorial optimization

relaxation due to entropy effects, temperature-

problem in terms of an Ising model, anddependent effective interactions, and local

searches for low-energy states of the Ising

magnetic fields. As a result, it was confirmed

model. Since the Ising machine operates on thethat there is a change in the time scalebasis of stochastic behavior, it is necessary to

depending on the bit-width to be reduced [4].

develop various internal algorithms to improve
the solution accuracy. In this research project,(II) Black-box optimization using Ising

we investigated algorithms to improve themachines

performance of the Ising machine from theAn Ising machine is a piece of hardware thatviewpoint of statistical mechanics.

takes an Ising model or equivalent Quadratic
Unconstrained Binary Optimization (QUBO)(I)

Dynamical

reduction

properties

algorithms

of

for

bit-width

representation as an input format and operates

simulated-

to find its ground state. Ising machines haveannealing-based Ising machine

been

appliedinvarioussituations

tohardwarecombinatorial optimization problems that canlimitations. We have focused on cases where

be represented by Ising models or QUBOs. On

thethe other hand, a method to use Ising machinesIsingmachinespossiblehavevaluesseveraloftheinteraction

coefficients and local field coefficients in theforIsing model are limited and have proposed a

discrete optimization problems where themethod to make Ising machines work properly

objective function is not explicitly given, waseven in the presence of such limitations [1].

proposed by us in 2020 [5]. We extended it andReference [1] confirms the validity of the

proposed a method to perform integer-valued

proposed method by comparing the groundblack-box optimization with Ising machines [6].

states. On the other hand, the dynamic

336

black-box

discrete

optimization,i.e.,Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

References

arXiv:2304.12796 (2023).

[1] D. Oku, M. Tawada, S. Tanaka, and N.

[5] K. Kitai, J. Guo, S. Ju, S. Tanaka, K. Tsuda,

Togawa, IEEE Trans. Comp. 71, 223 (2020).

J. Shiomi, and R. Tamura, Phys. Rev. Res. 2[2] S. Miyashita, S. Tanaka, M. Hirano, J. Phys.

(2020) 013319.

Soc. Jpn. 76, 083001 (2007).

[6] Y. Seki, R. Tamura, and S. Tanaka,[3] S. Tanaka and S. Miyashita, J. Phys. Soc.

arXiv:2209.01016 (2022).

Jpn. 78, 084002 (2009).
[4] S. Kikuchi, N. Togawa, and S. Tanaka,337

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Charge distribution in metallic and charge order phase inorganic crystals
Masatoshi SAKAI, Hiroki WATANABE, Ryosuke ANDO, Ryo WATANUKI
Department of Electrical and Electronic Engineering,
Chiba University, 1-33 Yayoi-cho Inage, Chiba 263-8522

Ourresearch

group

conductsof the parameter set, which can obtain thedevelopments of novel electronic devices

charge order phase and metallic phase. Next,

using organic charge order materials. In our

we are planning to try calculating real-time

previous work, we observed obvious fieldevolution from the insulator to the metallic

effect transistor (FET) characteristics of -

phase and vice versa..

(BEDT-TTF)2I3[1] with 4-probe electrical

Furthermore, we also proceeded themeasurement and small FET modulation of -

calculation of the charge order phase of -(BEDT-TTF)2PF6 [2] with 2-terminal electrical(BEDT-TTF)2PF6 crystal. This calculation

measurement. To analyze these experimental

started from the first-principle calculation

results, we are trying to calculate the(Quantumelectronic state of -(BEDT-TTF)2I3 and -parameters using RESPACK. By using the(BEDT-TTF)2PF6. This report is a interim

obtained parameters, we tried to represent thereport of this research work in progress.

charge distribution of the experimentally

Espresso),

and

we

obtained

At first, about the electronic state of -observed charge order. At present, we are(BEDT-TTF)2I3 crystal, we adopted new

making a superlattice structure to represent

parameter sets[3] of t, U, V because thethe real charge order. We are also trying toprevious parameter set could not reproduce

calculate the process and probability between

the experimentally observed charge orderthe insulator and metallic phase of -(BEDT-

structure. This time we adopted the parameterTTF)2PF6 to analyze our phase transition

set considering the breaking of inversion

device characteristics.

symmetry.

By

adopting

these

novel

parameters, we obtained calculated charge[1] under preparation

distribution of which the charge distribution[2] M. Sakai et al., Nanosci. and Nanotechnol. 16,

trend agrees with that of the experimentally

3267 (2016).

obtained one. And we could marshal the trend[3] D. Ohki et al., Phys. Rev. B 107, L041108 (2023).

338

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Magnetic structures on the thin ﬁlms made by the
dipolar interaction and the frictional force stemmingfrom these structures
Hisato Komatsu
Kyushu University, Kasuga, Fukuoka 816-8580 ∗Masamichi Nishino and Yoshihiko Nonomura
National Institute for Materials Science, Tsukuba, Ibaraki 305-0044

We applied for the usage of the ISSP Supercomputer planning to investigate the magnetic
structures of the dipolar system. However, wehave not got notable results on this subject.
Instead, we studied a theoretical model of themagnetic friction.
Magnetic friction, the frictional force generated from the magnetic interaction between
spin variables, has been extensively studied inrecent years. These studies aimed not only toinvestigate the behavior of the magnetic systems, but also to reveal the microscopic mechanisms of the friction. For this purpose, many
types of theoretical models have been proposed
and investigated[1, 2].
In the case of the normal solid surfaces,
there is a well-known empirical law called theDieterich-Ruina law[3, 4]. In our previous
studies, we proposed models of magnetic friction that seemed to obey this law, at least in
the steady state[5, 6]. These studies revealed
that magnetic structures can behave as a kindof potential barrier that prevent the lattice motion. In the study of this academic year, we investigated a simpliﬁed model of the magnetic
friction to consider the ﬁnite-size eﬀect, which∗

Present address : Shiga-university, Hikone, Shiga
522-0069

cannot be discussed in our previous studies.
Speciﬁcally, we introduced a model composedof N Ising spin variables {σi } interacting witheach other by the following Hamiltonian:H=−

J(x) ∑σi σj = −N J(x)m2 .
N i,j

(1)

Here, x is a real value representing the shift ofthe lattice, m is the magnetization per spin:∑m ≡ i σi /N , and J(x) is the periodic function of x. For simplicity, we impose a periodic boundary condition on x. As a concrete
form of J(x), two types, A and B, are considered. Type-A is a piecewise linear function,and type-B is a sinusoidal function.
We introduce the time development of spin
variables by the Glauber dynamics, and deﬁne one Monte Carlo step (MCS) as the unit
time. In addition, the time development of x
under this Hamiltonian and external force Fex
are introduced as the following overdamped
Langevin equation:
γN

dx
∂H √
= Fex −
+ 2γN T R(t),
dt
∂x

(2)

where R(t) is the white Gaussian noise,⟨R(t)R(t′ )⟩ = δ(t − t′ ). Here, we introduce
coeﬃcient N , considering the situation that N
spins move simultaneously.

339

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

References(a) 1.0

10-2
10-3

|m|

We investigated this system by numericalsimulation using ISSP Supercomputer, andfound that this system shows two diﬀerent behaviors depending on the damping constant γ.
When γ is small, the stick and slip states are
separated as the metastable states, and theprobability that each state appears determines
the relation between Fex and the lattice velocity v. When γ is large, on the other hand, suchseparation does not appear and v is determinedby the thermal activation process. We calculated the histogram of m and x like Fig. 1 andthe Fex -v relation, by the simulation. Furthurmore, we also estimated these quantities undersuﬃciently large or small γ theoretically using
approximations, and compared it with the result of the simulation.

10-4

0.5
10-5
10-6

0.0

10-7

-3 -2 -1

0

1

2

3

x

(b) 1.0

10-2[1] D. Kadau, A. Hucht, and D. E. Wolf,
Phys. Rev. Lett. 101, 137205 (2008).

|m|

10-3

[2] M. P. Magiera, L. Brendel, D. E. Wolf,and U. Nowak, Europhys. Lett. 87, 26002(2009).

[5] H. Komatsu, Phys. Rev. E. 100, 052130(2019)[6] H. Komatsu, Phys. Rev. E. 102, 062131(2020)[7] B. N. J. Persson, O. Albohr, F. Mancosu,
V. Peveri, V. N. Samoilov, and I. M. Sivebaek, Wear 254 835 (2003).

0.5
10-5
10-6

[3] A. Ruina, J. Geophys. Res. 88, 10359(1983)[4] J. H. Dieterich, Tectonophysics 144, 127(1987)

10-4

0.0

10-7

-3 -2 -1

0

1

2

3

x
Figure 1: Examples of the histogram of themagnetization m and shift of the lattice x under (a)small γ and (b)large γ. In the case that
γ is small, stick and slip states are separated
as the metastable states like graph (a). Thesegraphs are the results of the type-A model,
type-B model also shows the similar result.

340

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Numerical Study of One Dimensional Frustrated
Quantum Spin Systems
Kazuo HIDA
Professor Emeritus, Division of Material Science,
Graduate School of Science and Engineering,
Saitama University, Saitama, Saitama 338-8570

1

Model

We investigate the ground-state phases of
mixed diamond chains described by the following Hamiltonian:
H=

L [
∑

(1)

(1 + δ)S l (τ l(1)

+ τ l−1 )l=1(2)+ (1 − δ)S l (τ l
(1)

(2)

(1) (2)]+ τ l−1 ) + λτ l τ l

, (1)

(2)

where S l , τ l and τ l are spin operators with(1)
(2)
magnitudes Sl = τl = 1/2 and τl = 1,
respectively. The number of unit cells is denoted by L, and the total number of sites
is 3L. The lattice structure is depicted inFig. 1. We consider the region λ ≥ 0 and
(1)

τl
1+δ

periodicity of the ground state. Since the spin
operators τ (1) and τ (2) have diﬀerent magnitudes, however, it is not natural to assume
δ = 0 in a realistic situation. Hence, we examine more general cases with δ ̸= 0 in thepresent work.

(1)

1+δ

S=τ =1/2

Sl+1
λ
Sl
1−δ
1−δ
(2)
τl

(2)

τ =1

Figure 1: Structure of the diamond chain investigated in this work.
(1)

(2)

1 ≥ δ ≥ −1. For δ = 0, τ l + τ l commutes with the Hamiltonian (1) for all l. In[1], we made use of this property to determine
the ground-state phase diagram. In addition to
the Lieb-Mattis(LM) type ferrimagnetic phaseand the nonmagnetic Tomonaga-Luttinger liquid (TLL) phase, an inﬁnite series of ferrimagnetic phases with spontaneous magnetizations
msp = 1/p, where p(= 1, 2, ..., ∞) is the spatial

341

2

Analytical Results

Let us start with the analytic consideration ofseveral limiting cases.
(1)

1. λ ≫ 1 + δ, 1 − δ: All pairs of τ l and(2)
τ l form spin-1/2 states. If the eﬀective
interaction between them is antiferromagnetic, the ground state is a TTL state.
If it is ferromagnetic, the ground stateis the LM-type ferrimagnetic state with
msp = 1. Using the perturbation calculation with respect to (1 ± δ)/λ, we ﬁnd thephase boundary between these two phases
is given by δ = 0.6.
2. λ ≤ 0、1 ± δ > 0: The system is unfrustrated and the ground state is the LMtype ferrimagnetic state with msp = 1.
A similar ground state is expected for
0 < λ ≪ 1 ± δ.
3. 1 + δ ≃ 0 and λ ≃ 0 : For 1 + δ = λ = 0,(2)
Sl snd τ l form a ferrimagnetic chain(1)
with msp = 1/2 and τ l are free spinswith magnitude 1/2. For small 1 + δ andλ, we can determine the phase boundary between the LM-type ferrimagnetic

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

phase with msp = 1 and the nonmagnetic phase as λ/(1 + δ) ≃ 0.738 by the
perturbation calculation with respect tothese small parameters 1 + δ and λ combined with the numerical exact diagonalization (NED) calculation of the ferrimagnetic chain.
4. 1 − δ = 0 or λ = 0 : If 1 − δ = λ = 0,
(1)
S l and τ l form a spin-1/2 antiferromag(2)netic Heisenberg chain aand τ l are freespins. If λ > 0 and δ = 1, the ground stateis the LM-type ferrimagnetic phase with
msp = 1. The same is true if δ < 1 and
λ = 0. However, if λ > 0 and δ < 1, these
two interactions frustrate, and a nontrivial ground state is realized.

3

Numerical Results

ferrimagnetic phases with msp = 1 are not asingle phase, but are separated by the partial
ferrimagnetic phase and nonmagnetic phase.
The nonmagnetic phase continues to the TLL
phase for large δ. Hence, the whole nonmagnetic phase is likely to be a TLL phase. However, since the sum of the spin magnitudes in aunit cell is an integer, the Lieb-Schultz-Mattis
theorem does not exclude the gapped phase.
The fate of the inﬁnite series of ferrimagnetic
phases found for δ = 0 in [1] is also investigated. The λ-dependence of the spontaneous
magnetization for δ = 0.1 is shown in Fig. 3.
It seems that the width of the ferrimagnetic
phase with msp = 1/2 (p = 2) remains ﬁnite,while those with p ≥ 3 are smeared out andthe corresponding region turns into the partial
ferrimagnetic phase suggesting the fragility of
the ferrimagnetic phases with large p.
msp

We have carried out the NED calculation for
L = 4, 6 and 8 and the DMRG calculation for
L = 24 and 48. The magnetization curves
and spontaneous magnetization are calculated
to obtain the ground-state phase diagram ofFig. 2. It should be remarked that the two

p=1

0.5

msp

δ=0.1
L=48

1

p=1

1

p=2

0.5

p=2

p=3

p=3

p=4

~0.807λc(∞)−

p=4

p≤38

0
0.6

0.7

0.8

λ 0.9

0
0.6

0.7

λ

0.8

2
λ

1+δ

λ
1/2

1/2

λ
1−δ

nonmag (gapless?)

1

Figure 3: Left panel : λ-dependence of the
spontaneous magnetization in the ground statefor δ = 0.1 calculated by the DMRG method
for open chains with L = 48. Right panel: The
corresponding ﬁgure for δ = 0 taken from ref.
[1].

1+δ

1−δ

msp=1

λ
1−δ

msp=1/2
partial
ferri

msp=1
1+δ
λ
1−δ

0
−1

0

δ

1

References

Figure 2: Ground-state phase diagram. The
open circles are the phase boundaries estimated from the NED data extrapolated to thethermodynamic limit. The ﬁlled circles are thephase boundaries estimated from the DMRG
data for L = 24 and 48. The curves are guides
for the eye. The insets are schematical spin
structures in diﬀerent phases.

342[1] K. Hida, J. Phys. Soc. Jpn. 90, 054701(2021).

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Novel phenomena in mismatched multilayer systems: Search
for new candidate materials and analysis of electronic
structures
Toshikaze KARIYADO
Research Center for Materials Nanoarchitectonics,
National Institute for Materials Science, Namiki, Tsukuba, Ibaraki 305-0044
a way to valleytronics applications.

Recently, artificially stacked 2D systems
attract much attention as a playground for

In the analysis of monolayer and bilayerelectronic structure manipulations. In particular,

BC3, we make full use of the ab initio method,

systems with nanoscale moiré pattern induced

using mainly Quantum Espresso and sometimes

by

are

OpenMX. A characteristic feature of moiréinteresting, since the nanoscale patterns tend tobilayer systems is that the relation between two

have striking effects on low energy band

layers depends on position, and it is important

structures.

to know the local crystalline and electronicslightmismatchbetweenlayersIn this project, we propose twisted bilayer

structures to build a model to handle theBC3 as a system for realizing interesting valley

nanoscale moiré pattern. This means that we

related physics [1]. BC3 is a graphene

need to work out computations on various local

derivative with a honeycomb network of atoms

structures[2]. The monolayer BC3 is a three-valley

positions in the nanoscale moiré unit cell. Then,

semiconductor with its conduction minima atlarge computational resources are required, andthree M-points in the hexagonal Brillouin zone.

the numerical efficiency is important. For the

Each of the valleys shows a 2D parabolic

numerical efficiency, the Quantum Espresso

dispersion around the conduction minima,

package offers an efficient parallelized version.

corresponding

to

the

variousthough there is anisotropy. It is found that uponmaking twisted bilayers, the 2D dispersion issqueezed into quasi-1D one. Interestingly, theReferences

quasi-1D directionality depends on the valley[1] T. Kariyado, Phys. Rev. B 107, 085127

degrees of freedom. That is, twistronics applied(2023).

on bilayer BC3 leads to valley dependent band[2] H. Tanaka et al., Solid State Commun. 136,

structure manipulation, which potentially paves

22 (2005).

343

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Structure analysis of borophene by using 2DMAT
Akari TAKAYAMA
Department of Physics,
Waseda University, Ohkubo, Shinjuku-ku, Tokyo 169-8555
TRHEPD were performed by the structure-

Introduction

analysis program, “2DMAT” [4], which is

Atomic-layer sheet composed of group XIV

included as standard software in ISSP’s

elements such as graphene and silicene havesupercomputer.

been actively studied. These atomic-layer sheets

Using

massiveparallelcomputer simulations on supercomputer is very

form a honeycomb structure and are expected to

useful for structure analysis of large periodic

be applicable to various technologies because ofstructure and surface superstructure with lattice

their excellent electron mobility due to theirreconstruction because they require many

peculiar band dispersion following the Diracvariables for analysis.

equation. Recently, an atomic-layer sheet
material of boron (the group XIII element),

Results and discussion

called “borophene” is attracting attention,
especially after the success of its fabrication on

The TRHEPD experiment consists ofmetal substrates. Unlike atomic-layer sheets ofmeasuring a series of the diffraction patterns forgroup XIV elements that form a honeycomba fixed incident azimuthal direction at various

lattice, flat layers of boron are theoreticallyglancing angles (). Here, the rocking curve isstable when composed of a triangular lattice anddefined as the diffraction intensity of the (00)hexagonal hollows. Experimental observations

spot plotted as a function of . In the structuralof epitaxial borophene on Ag(111) have been

analysis, the experimental rocking curves are

consistent with the theoretical structural models

compared with those calculated for various[1, 2], but the atomic structure has not been

structural models. Figure 1 shows the rocking

examined directly. More interestingly, scanning

curves of (a) a pristine Ag(111) surface and (b)tunneling microscopy (STM) observations of athe borophene grown on it, measured under thehoneycomb structure on an Al(111) substrate
were reported [3], but the detailed atomic
structure of this honeycomb lattice is unclear.
Thus, there is a strong need to conduct structural
analysisbyanappropriate

diffraction

experiment on the surface.
In our research, we studied the atomic
structure of borophene on Ag(111) and on
Al(111) by means of total-reflection high-energy

Fig. 1: Rocking curves under one-beam

positron diffraction (TRHEPD). For structure

condition with calculated curves for (a)analysis, calculations of the rocking curves forAg(111) and (b) borophene on Ag(111).

344

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

one-beam condition. In the one-beam condition,
the beam is incident along an off-symmetricdirection, thus the rocking curve in the one-beamcondition essentially gives the information on
the atomic positions in the out-of-plane direction.
The experimental profile was reproduced by the
proposed model [2]; layer distance between Ag

Fig. 2: Rocking curves under one-beamand borophene is 2.4 Å and the magnitude of

condition for (a) before and (b) after boron

bucking for borophene 0.04 Å. This indicatesdeposited on Al(111).

that there is no buckling, and we have directly
confirmed that the borophene is a flat sheet [5].
Figure 2 shows the rocking curves of (a) aReferences

pristine Al(111) surface and (b) the borophene[1] R. Wu, et al., Nature Nanotechnology, 14grown on it under the one-beam condition(2019) 44.

obtained from the experiment and from

[2] G. P. Campbell, et al., Nano Letters, 18

calculation of Al(111). After boron deposition,(2018) 2816.

the peak position shifted slightly, but the whole[3] W. Li, et al., Science, 63, (2018) 282.

shape of the rocking curve did not change much.

[4] Y. Motoyama, et al., Computer Physics

According to angle-resolved photoemission

Communications, 280 (2022) 108465.

spectroscopy (ARPES) measurements and band[5] Y. Tsujikawa, AT et al., Molecules. 27

calculations for borophene on Al(111) substrates,(2022) 4219.

it is predicted that the borophene forms AlB2 at[6] D. Geng, et al., Physical Review B 101

the top surface [6]. However, the rocking curve(2020) 161407(R).

of the AlB2 model differs significantly from our[7] A. B. Preobrajenski, et al., ACS Nano, 15,

experimental results, which is inconsistent with

(2021) 15153.

the results of this study. One possible reason forthe difference between our experiment andprevious studies is the effect of ripple structure[3]. Since the ripple structure was not consideredin this analysis, it is possible that the model usedin the analysis did not correctly reproduce theexperimental results. It has also been pointed outthat borophene on Al substrate forms longperiod moiré structures due to lattice mismatchwith the substrate [7]. To determine the structureof honeycomb borophene on Al substrate, wewill analyze the giant lattice system utilizing
parallel computation on a supercomputer.

345

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Magnetic structures of multiple-Q orders ininversion-symmetric Hubbard Models
Takashi UCHIDA
Hokkaido University of Science
4-1, 7-15, Maeda, Teine-ku, Sapporo 006-8585

In the last decade, multiple-Q orders inmagnetic systems have attracted attention because they sometimes reveal topologically protected magnetic structures such as magnetic
skyrmions. Typically, these structures are realized in the Dzyaloshinskii-Moriya (DM) interaction driven systems under magnetic ﬁeld.
The multiple-Q orders can also be realized ininversion-symmetric systems where the DM interaction is absent [1, 2].
In order to investigate the nature of themultiple-Q orders in inversion-symmetric itinerant systems, we have explored the magnetic
structures of the triangular-lattice single-band
Hubbard model by means of the molecular spin
dynamics (MSD) method [3]. The present formulation of the MSD adopts the static approximation to the functional integral method andthe isothermal molecular dynamics technique,and reduces to the generalized Hartree-Fockapproximation at the ground state.
In the present work, we have performed the
MSD calculation with use of the global charge
neutrality condition instead of using the local charge neutrality condition [3]. This has
the advantage that the charge transfer among
the atoms is allowed in the magnetic structure formation. In the numerical calculations
the most time-consuming process is the magnetic force calculation at each time step, where
the local electronic structures are calculated inreal space by means of the recursion method.
We have adopted the MPI parallel calculation
scheme and found it eﬀective in saving bothcomputing time and CPU resources.
We have performed magnetic structure
calculations on a hexagonal supercell with
972 lattice points, which is embedded in alarge cluster consisting of 6 such supercells,
each of which are connected by the periodic
boundary condition. Under zero magnetic
ﬁeld and the ﬁxed value of the temperature
T /t = 0.0005, we have explored the magnetic structures changing the Coulomb interaction strength U/t and the electron number
n along the antiferromagnetic-ferromagnetic
boundary. We have found that the stablestates for U/t < 4.0 and n = 1.45 ∼ 1.53
are the 3Q magnetic orders with the Q vectors
pointing in the direction of three axes of thetriangular lattice and that they are accompanied by 3Q charge density waves (CDW) withthe same Q vectors as the magnetic ones. The
present results with use of the global charge
neutrality condition reveal the enhancement of
the amplitude of the main 3Q magnetic order as compared to those with use of the local
charge neutrality condition, showing that the
CDW contributes to stabilize the magnetic 3Q
order.

References[1] T. Okubo, S. Chung, and H. Kawamura:Phys. Rev. Lett. 108 (2012) 017206[2] Y. Kakehashi: J. Phys. Soc. Jpn. 89 (2020)
094710.
[3] Y. Kakehashi, S. Akbar, and N. Kimura:Phys. Rev. B57 (1998) 8354.

346

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Development of integrated interface of eigensolvers
Rokko and application to quantum spin systems
Tatsuya Sakashita
Center for Quantum Information and Quantum Biology, Osaka University
1-2 Machikaneyamachō, Toyonaka, Osaka 560-0043

To establish universal exact diagonalization
package for quantum lattice models including the Heisenberg-Kitaev model, we focusedon developing integrated interfaces for eigensolvers, “Rokko”[1].
In Rokko, we implemented the integrated interfaces for the following types:• Serial solvers for dense matrices (Eigen3,
LAPACK)• MPI parallelized solvers for dense matrices (EigenExa[2], ELPA[3], Elemental[4],ScaLAPACK)• MPI parallelized solvers for sparse matrices (Anasazi in Trilinos[5], SLEPc[6]) tocover matrix representations below:– CRS (Compressed Row Storage)– Matrix-free method (the method togive matrix-vector product routines
to solvers)• Unit and integrated test programs by
GoogleTest• Install scripts of eigensolvers for various
architectures
We prepare a paper to report design policy, software structure, and usage examples ofRokko.

References[1] T. Sakashita, R. Igarashi, Y. Motoyama,
T. Okubo, and S. Todo.
Repository of Rokko.
https://github.com/
t-sakashita/rokko.git, 2012.
[2] T.
Imamura,T.
Hirota,andT. Fukaya.
EigenExa web page.
https://www.r-ccs.riken.jp/labs/
lpnctrt/projects/eigenexa/, 2021.

Rokko has the following features:[3] ELPA Consortium. ELPA (Eigenvalue
soLvers for Petaflop Applications webpage). http://elpa.rzg.mpg.de, 2013.

• Integrated interfaces for eigensolvers andmatrices, independent of individual eigensolver libraries[4] J. Poulson. Distributed-memory dense linear algebra Elemental web page. https://github.com/elemental/Elemental, 2013.

• Rokko’s interfaces are implemented byutilizing factory. It enables the user todynamically select a solver.

[5] M. A. Heroux, R. A. Bartlett, and V. E.
Howle. Trilinos Project web page. https://trilinos.github.io, 2003.

• C, Fortran, and Python bindings of Rokko[6] V. Hernandez, J. E. Roman, and V. Vidal.
SLEPc web page. http://slepc.upv.es,
2002.

• Automatically detecting libraries by using
CMake in building Rokko

347

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Systematic understanding of the slip behaviors ofslow and fast earthquakes and the eﬀect of the upper
value of porosity
Takehito SUZUKI
Department of Physical Sciences, Aoyama Gakuin University
5-10-1, Fuchinobe, Chuo-ku, Sagamihara, Kanagawa 252-5258

Two qualitatively diﬀerent behaviors ofearthquakes have been known. One is thefast earthquakes, which we feel naturally. The
other one is slow earthquakes, which generate
negligible seismic waves. Although the slow
earthquakes are not disastrous, they are considered to sometimes change to the fast earthquakes after several repetitions. Whether thistransition occurs or not is a fatal problem tothe human society. Systematic understanding
for the slip behaviors of the slow and fast earthquakes is aimed in this study.
The fault rocks can be considered as porous
media, and the pores within the media are usually considered to be ﬁlled with water. If the
dynamic earthquake slip (frictional slip) occurs, we can consider the interaction among
heat, ﬂuid pressure, and porosity [1, 2]. The
slip is interpreted as the deformation aroundthe contact area, called slip zone, which has aﬁnite width wh . During the dynamic slip, thegeneration of pores [frictional heating] reduces[raises] the ﬂuid pressure in the slip zone, inducing the increase [reduction] in the friction
stress and the reduction [increase] in the slip
velocity [1, 2]. The ﬂuid-pressure increase due
to frictional heating will be called FH eﬀect,
whereas the ﬂuid-pressure decrease due to thepore generation is referred to as PG eﬀect. The
slow and ordinary earthquakes are dominatedby the PG and FH eﬀects, respectively.
The spring-block model and the interaction
between three quantities are employed to in-vestigate both the slow and fast earthquakes.
This system repeats the dynamic slip and cessation. The healing of pores in the cessation
time is also taken into account.
With this system, we found an importantfunction determining the system behavior [3].
For the derivation of the function, we employed
three important assumptions: (i) we consider asingle block, (ii) the diﬀusions of heat and ﬂuid
are neglected, and (iii) the change in the bulk
modulus of the medium, M0′ , due to porosity change is neglected; i.e., M0′ (ϕ) = M0′ (ϕ0 ),where ϕ0 is the porosity at the onset of the slip.
With these assumptions, we found a functionof the slip uf ,1
F (uf ) = kp u2f + µstat (σn0 + p0 )uf
2
[ (
)
1
M0′ α00−µslid(1 − e−γuf )σ n + p 0 − α0
−
γ
γ
ϕ∞
]

M ′ ϕ∞+ α0 0(1 − e−α0 uf /ϕ∞ ) , (1)−γϕ∞where kp is the spring constant, µslid and µstat
are the sliding and maximum static friction coeﬃcients, respectively, σn0 is the normal stressacting on the interface, p0 is the ﬂuid pressure at the onset of the slip, γ is the positive
constant, and α0 and ϕ∞ are the positive constants appearing in the porosity evolution law
(

)

∂ϕ
ϕ
wh
wh
= α0 v 1 −[H(y+ )−H(y− )].
∂t
ϕ∞
2
2
(2)

348

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Two solutions whose magnitudes are different in two orders emerge for the equation
F (uf ) = 0, and the small and large ones correspond to the slow and fast earthquakes, respectively. Detailed analysis shows that thisbehavior of the solution can be interpreted as
the phase transition of the ﬁrst order [3].
We should now remove above-mentioned
three assumptions to treat more realistic situation. We performed numerical calculations
with ISSP supercomputer. The number ofblocks is 40, and the diﬀusions of heat and ﬂuid
are included. Moreover, from Eq. (2), the assumption (iii) should be valid if the value of
ϕ∞ is negligibly small. We consider three values for ϕ∞ , and the condition where we canapply the analytical treatment will be clariﬁed.
If we assume ϕ∞ = 0.01, only the fast earthquakes occur, and the slow earthquakes do notoccur (Fig. 1a). This is because the porosity change is negligible, and the ﬂuid pressurechange due to the PG eﬀect is negligibly small.
If we assume ϕ∞ = 0.1, both the fast and slow
earthquakes occur (Fig. 1b) because both theFH and PG eﬀects work in this case. If we assume ϕ∞ = 0.9, the fast and slow earthquakes
occur (Fig. 1c). The FH and PG eﬀects work
also in this case.
Additionally, the analytical treatment using
F (uf ) was found to be eﬀective for ϕ∞ = 0.01
and 0.1, whereas it was not eﬀective for theslow earthquake observed for ϕ∞ = 0.9. The
value 0.9 may be too large to employ theabove-mentioned assumption (iii). The function F (uf ) is useful for predicting the dynamic
slip behaviors of the natural fault zones if ϕ∞is much smaller than unity.

References

(a)

(b)

(c)

Figure 1: The slip velocity of the 20th blockwith (a) ϕ∞ = 0.01, (b) 0.1, and (c) 0.9.

[1] T. Suzuki, and T. Yamashita: J. Geophys.
Res. 119 (2014)[2] T. Suzuki: Phys. Rev. E 96 (2017)[3] T. Suzuki, and H. Matsukawa, in prep.

349

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Shape dependence of macroscopic friction between solids
Michio OTSUKI
Graduate School of Engineering Science,
Osaka University, Machikaneyama, Toyonaka, Osaka 560-8531
Amonton's law states that the static friction

static friction coefficient and the behavior of

coefficient does not depend on the external

precursor slip [3].

pressure or the shape of the object. Recent
studies have shown that Amonton's law
systematically breaks down for macroscopic
objects due to precursor slip before the onset ofbulk sliding [1, 2]. However, it is unclear
whether the results apply to more realistic 3D
systems since the analysis in previous works is

Fig. 1 Schematic of a 3D viscoelastic block

restricted to 2D systems.

on a fixed rigid substrate.

First, we have numerically investigated thesliding motion of a 3D viscoelastic block on a

In addition, we have studied the effect of therigid substrate using finite element simulation as

shape of the frictional interface. Frictional

shown in Fig. 1. The maximum computational

interfaces of some industrial products, such as

size requires 500,000 nodes and 10 billion timeshoe soles and tires, are often grooved. However,steps, which takes 100 hours using our code

there is a need to understand how grooves affectcompiled with the Intel Fortran compiler and a

the frictional properties. Therefore, we haveparallel computation of 2,000 MPI processes on

numerically investigated the slip of objects that

the CPU server. The block exhibits stick-slip

are longitudinally grooved as shown in Fig. 2.

motion characterized by the static friction

The static friction coefficient is a decreasing

coefficient. The static friction coefficient

functiondecreases with increasing external pressure,

Furthermore, our theoretical analysis shows that

length, or width of the block, which contradicts

the decrease is caused by a reduction in the

Amonton's law. The precursor slip occurs in theeffective viscosity due to the groove. These

2D interface between the block and substrate

results provide new insight into the design of

before bulk sliding. The instability analysis of agrooves for friction control.

simplified model explains the decrease in the

350

of

groovewidth

anddepth.

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

References[1] M. Otsuki and H. Matsukawa, Sci. Rep. 3,
1586 (2013).
[2] Y. Katano, K. Nakano, M. Otsuki, and H.
Matsukawa, Sci. Rep. 4, 6324 (2014).
[3] W. Iwashita, H. Matsukawa, and M. Otsuki,
Sci. Rep. 13, 2511 (2023).

Fig. 2 Schematic of a 3D viscoelastic
block with grooves block on a fixed rigid
substrate.

351

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

3.5

SCCMS Projects352

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Developing graph neural networks that helps
predicting the molecular dynamics of glassy liquids
Hayato SHIBA
Information Technology Center, University of Tokyo
Kashiwa-no-ha, Kashiwa, Chiba 277-0882

In the past two decades, structure–dynamics
correspondence in glass-forming liquids havebeen hot topic. Along with the drastic slowdown of atomic motion accompanying the glasstransition, localized domains of les that rearrange more preferentially than others growup. Structural analyses of local geometric orders and of real-space normal modes of vibrations have clariﬁed the existence of correlations between the structure and the dynamics. It has remained, however, unclear to what
extent the dynamics can be dictated by the
structure. Recently, machine learning methodologies, in particular, graph neural networks(GNNs), enabled us to predict the slow dynamics far better than these methodologies. The
GNNs have turned out to be a good ﬁt formaking predictions more accurately than ever,and thus, open a new paths to further reveal
the structure-dynamics relationship.
In FY2022, we have engaged in improving
our model, BOTAN (BOnd TArgeting Network), which we have proposed in FY2021.
In BOTAN, a decoder for edge features is additionally implemented to the previous GNN
model to enable itself to learn relative motion
between neighbor pairs (edge target) in addition to self-motion of particles (node target).
This model has tens of thousands of weightparameters in it, which is the reason why it
can capture the complex relation between the
structure and the dynamics. However, in themeawhile, it is diﬃcult to train such a large
model, especially when edge features come in

0.03ταBapst GNN
FY2021
FY2022

T = 0.44
10-1

100

τα

101

102

t

103

104

105

Figure 1: Comparison of pearson correlationcoeﬃcients between diﬀerent GNN models:the original GNN model and of BOTAN (inFY2021 and FY2022).
as the training target. In order to ﬁx such
shortcomings, we have introduced a set of pretrained weight parameters that reﬂect the factthat closer pairs are more likely to separate
in a short-time range. This improvement has
updated the best predictive performance evenon the time-scale of glassy structural relaxation, see Fig. 1 in which the Pearson correlation coeﬃcients representing the predictive
accuracy (τα denotes the structural relaxationtime). BOTAN is now one of the state-ofthe-art models for glassy dynamics relaxation,
as well as other models that have also appeared in FY2022. The full article introducing his state-of-the-art model of BOTAN has
been published [1], together with training code
implemented on PyTorch Geometric [2].
In FY2022, we have started our next trial to353

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

develop a new application of BOTAN: learning
the dynamics of a glassy liquid under quasistatic shear at zero temperature. We havesimulated using so-called the Kob-Andersen binary glass-forming mixture and computed thedataset for training by using System B at ISSP,
and now we are trying to apply BOTAN for
the prediction of rearranging hotspot from theinitial structure.

References[1] H. Shiba, M. Hanai, T. Suzumura, andT. Shimokawabe: J. Chem. Phys. 158,
084503 (2023).
[2] https://github.com/h3-Open-BDEC/
pyg botan

354

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

A systematic ab initio study of quasi-one-dimensional
molecular conductors TM salts
Kazuyoshi Yoshimia , Takahiro Misawaa,b , Takao Tsumurayac , and Hitoshi Seod,eaInstitute for Solid State Physics, University of Tokyo, Kashiwa-no-ha, Kashiwa, Chiba 277-8581
b
Beijing Academy of Quantum Information Sciences, Haidian District, Beijing 100193, Chinac
POIE, Kumamoto University, 2-39-1 Kurokami, Kumamoto 860-8555, Japand
Condensed Matter Theory Laboratory, RIKEN, Wako, Saitama 351-0198, Japane
Center for Emergent Matter Science (CEMS), RIKEN, Wako, Saitama 351-0198, JapanThe quasi-one-dimensional organic conductor TM2 X (TM=TMTSF, TMTTF, X: monovalent anion) has been extensively studied experimentally and theoretically due to its diverse ground states induced by changes in pressure and molecular substitution[1, 2]. Whilethe importance of electronic correlation effects
has been pointed out in these studies, a systematic evaluation of electronic correlation effects has not been carried out due to the difficulty of quantitatively estimating electronicinteractions in molecular systems.
In this study, we derive effective models
using first-principles calculations as a starting point and the constrained random phaseapproximation method. The analysis of organic conductors involves molecules with many
atoms as a unit, which requires significant
computational effort and memory to derive
effective models. We addressed this problem by using the recently developed software
RESPACK[3] and exploiting its parallelization
feature to take advantage of large computing resources. Using the first-principles software Quantum ESPRESSO[4] and RESPACK,
we systematically derived effective models for
TM2 X and analyzed the electronic states using a many-variable variational Monte Carlosolver, mVMC [5].
Based on the obtained results, we discuss
the material dependence of the electronic cor-

relation effects in the TM molecular systemand clarify the relationship between the different ground states observed in the experimentand the electronic correlation effects. We showthat the correlation effect in this system can becharacterized by the screened on-site Coulombinteraction U relative to the intrachain transfer integrals. This parameter is material dependent and we show that it plays a key rolein understanding the spin and charge ordering
phenomena in the system[6].
We thank R. Kato, S. Kitou, and T. Nakamura for fruitful discussions.
This work
was supported by MEXT/JSPJ KAKENHI
under grant numbers 19K03723, 19K21860,
20H04463, 21H01041 and 22K03526.
KY
and TM were supported by Building of Consortia for the Development of Human Resources in Science and Technology, MEXT,
Japan.
The input and output files for
RESPACK and mVMC are uploaded to ISSP
data repository[7].

References[1] D. Jerome, Chem. Rev. 104, 5565 (2004).
[2] K. Yoshimi, H. Seo, S. Ishibashi, and S.
E. Brown, Phys. Rev. Lett. 108, 096402(2012), and references therein.

355

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo[3] K. Nakamura, Y. Yoshimoto, Y. Nomura
et al., Comp. Phys. Commun. 261, 100781(2021).
[4] P. Giannozzi, O. Andreussi, T. Brumme
et al., J. Phys.: Condens. Matter 29,
465901 (2017).
[5] T. Misawa, S. Morita, K. Yoshimi et al.,Comp. Phys. Commun. 235, 447 (2019).
[6] K. Yoshimi, T. Misawa, T. Tsumuraya,
and H. Seo, arXiv:2210.13726.
[7] https://isspns-gitlab.issp.utokyo.ac.jp/k-yoshimi/tm-salts

356

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Development of an effective model estimation tool by
Bayesian optimization
Kazuyoshi Yoshimi
Institute for Solid State Physics, University of Tokyo
Kashiwa-no-ha, Kashiwa, Chiba 277-8581

Target vs Estimated magnetization curve
0.5

TargetEstimated (BO_NO13)

0.4
0.3
Sz

Analysis based on effective models is useful to understand properties of materials. The
reliability of an effective model is often evaluated by comparing it with experimental data,
but this requires trial and error due to the existence of the large number of model parameters.
We have developed a tool called Bayesian optimization tool of Effective Models (BEEMs)[1] to estimate effective models as an inverse
problem using experimental data and Bayesian
optimization with Gaussian processes [2].
In BEEMs, the quantum lattice model solverHΦ[3] is used as the forward solver and themagnetization curves are calculated based on
the given Hamiltonian. The cost function isthe difference between the obtained magnetization curve and the target magnetization
curve. We use the Bayesian optimization library PHYSBO[4] to find the next candidate
point of the Hamiltonian that may have asmaller cost function.
BEEMs is available from the GitHub pageand can be installed by the following procedures:

0.2
0.1
0.0
0.0

0.5

1.0

1.5
H

2.0

2.5

3.0

Figure 1:
The magnetization curve at(J2 , J3 ) = (0.1, 0.02) (represented by red symbols).The proposed magnetization curve using
BEEMs (represented by the blue line).

is a tool for plotting magnetization at specified
candidate points. output_score is a tool for
displaying the score and the candidate point
parameters up to the specified rank.

As a demonstration, we performed model
parameter fitting for S = 1/2 Heisenberg spin
chain with 12 sites under periodic boundary
condition. The Hamiltonian is given by H =P12i=1 [J1 Si · Si+1 + J2 Si · Si+2 + J3 Si · Si+3 ].
$ git clone
For simplicity, we set J1 = 1 and calculated
https://github.com/k-yoshimi/BEEMs.git
the magnetization curve at J2 = 0.1 and$ cd BEEMs
J3 = 0.02 as the reference data as shown in$ pip3 install .
Fig. 1. Figure 2 shows the mean squared error
Then, you can use beems as a python pack- (MSE) between the reference data and theage. Post tools gauss_fit, mag_plt, and calculated data. Up to the fifth step, ranoutput_score are also available. gauss_fit dom sampling is performed even if Bayesianis a tool for constructing a score function us- optimization is selected. It is seen that aftering a Gaussian process based on the results performing Bayesian optimization, the cost
obtained by Bayesian optimization. mag_plot function reaches almost zero in the 13th step,357

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

0.008

BO (from 5 steps)

0.5

Random sampling

0.4

0.006
0.004
0.002
0

MSE

0.3

J3

Mean Squared Error (MSE)

0.01

0.2
0.1

0

5

0

10 15 20 25 30 35 40 45 50
Steps

Figure 2: Mean squared error(MSE) between
the reference data and the calculated dataobtained by the Bayesian optimization (BO)and random sampling, respectively. The green
shaded area indicates the region where the random sampling is performed.
while random sampling is underperformed
comparing to Bayesian optimization evenafter 50 steps. The optimal parameters foundby Bayesian optimization for the best score
are J1 = 1.0, J2 = 0.1, and J3 = 0.025. We
created a trained model using gauss_fit.py
to estimate MSE obtained with the Gaussian
process after 50 trials. As a result, we foundthat the region where (J2 , J3 ) is relatively
small has a lower score as shown in Fig. 3.
Using such a map, it is possible to check theaccuracy of the estimated parameters. For
example, we could explore approaches such as
refining the grid size in the low score region.
In summary, we have developed BEEMs: aneffective model estimation tool by combiningHΦ and PHYSBO. In version 1.0, it is possible
to estimate the effective model based on themagnetization curve. In the future, we aim to
improve the calculation speed, as currently all
magnetic fields have to be calculated. We plan
to add a mode that can calculate the magnetization curve by splitting the calculation by Sz
when Sz is conserved. We also plan to extend
the tool to estimate other physical quantities
if there is demand. We also plan to provide anexpert level manual and allow users to define
their own classes for use.

0

0.2 0.4 0.6 0.8

J2

1

0.018
0.016
0.014
0.012
0.01
0.008
0.006
0.004
0.002
0

Figure 3: The color map of MSE estimated by
the trained model using gaussian process.
We would like to acknowledge T. Misawa,
R. Tamura, and Y. Yamaji for their contribution to code development and useful discussion. KY was supported by JSPS KAKENHI
Grant Number 21H01041.

References[1] https://github.com/k-yoshimi/BEEMs[2] C.E. Rasmussen and C.K.I. Williams,
Gaussian Processes for Machine Learning,
The MIT Press (2005).
[3] M. Kawamura, K. Yoshimi, T. Misawa
et al., Comp. Phys. Commun. 217, 180(2017).
[4] Y. Motoyama, R. Tamura, K. Yoshimi
et al., Comp. Phys. Commun. 235, 447(2019).

358

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Exciton Properties in Organic Optoelectronic Devices
from Large-Scale Electronic Structure Calculations
Takatoshi Fujita
National Institutes for Quantum Science and Technology
Anagawa, Inage-ku, Chiba 263-8555

Accurate calculations of electronic states
are essential for computational studies of organic materials which are directed toward understanding of fundamental processes in organic electronic devices.
We have developed the novel large-scale excited-state methods [1] based on the fragment molecular orbital(FMO) method, exicton model, and GW and
Bethe-Salpeter equation (BSE) method with
Tamm-Dancoﬀ approximation (TDA). In thismethod, the excited-state Hamiltonian matrix
elements at the GW/TDA-BSE level is calculated in the basis of fragment conﬁguration
state functions which describe intrafragment
excitations or interfragment charge-transfer
excitations. The excited-state Hamiltonian isthen diagonalized to approximate the adiabatic excited states of an entire system. The
novel fragment-based GW/BSE method allows for the eﬃcient calculation of delocalized excited-states in realistic molecular aggregates. In addition, the excited-state Hamiltonian derived this method can be used forreal-time simulations in combinations with the
wavepacket prorogation method.

model Hamiltonian for single-excitation anddouble-excitation manifolds was derived on thebasis of fragment-based excited-state calculations within the GW approximation and theBSE. After elucidating the energetics of theelectron–hole separation and examining linear absorption spectrum, we investigated thequantum dynamics of exciton and charge carriers in comparison with the pump–probe transient absorption spectra. In particular, we
introduced the pump–probe excited-state absorption (ESA) anisotropy as a spectroscopic
signature of charge carrier dynamics after exciton dissociation. We found that the charge
separation dynamics can be probed by thepump–probe ESA anisotropy dynamics after
charge-transfer excitations. The present study
provides the fundamental information for understanding the experimental spectroscopy signals, by elucidating the relationship between
the excited states, the exciton and charge
carrier dynamics, and the time-resolved spectroscopy.

We have developed a computational method
for excited-state dynamics and time-resolved
spectroscopy signals in molecular aggregates [2], on the basis of ab initio excitedstate calculations. As an application, we
consider the charge separation dynamics andpump–probe spectroscopy in the amorphous
P3HT/PCBM blend. To simulate quantum
dynamics and time-resolved spectroscopy, the[1] T. Fujita, Y. Noguchi, J. Phys. Chem. A
125 (2021) 10580.

References[2] T. Fujita, T. Hoshi,
https://doi.org/10.26434/chemrxiv-2023773xs.

359

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Development of an efficient method for calculating
miscibility of long-chain polymer blends using molecular
dynamics simulations
Kazuo Yamada and Nobuyuki Matubayasi
Division of Chemical Engineering, Graduate School of Engineering Science,
Osaka University, Toyonaka, Osaka 560-8531
We have studied the mutual miscibility ofof a theory of solutions (energy-representation

polymer species in polymer mixtures using all-method). In our method, we compute theatom molecular dynamics simulations. The

monomer-wise chemical potential ∆𝜇!"#$% formutual miscibility of polymers is determined

the incremented monomers in the taggedby the free energy of mixing given by thepolymer and the total free-energy of solvationdifference between the chemical potentials of∆𝜇 of the polymer is the sum of ∆𝜇!"#$% . This

the pure and mixed states of the constituent

year, we focused on the computation of the freespecies. Since the chemical potential isenergy of solvation in polymer mixture systems.

sensitive

tointra-

andintermolecularinteractions, an atomistic treatment of polymer

Results

mixture systems is desired to evaluate free

The free energy was computed for the mixing

energyatomisticof polyethylene (PE) and polyvinylidene

calculation of the chemical potential of an

difluoride (PVDF) mixture systems with theentire polymer chain is a difficult task withmolar ratio 1:0, 3:1, 1:1, 1:3, and 0:1. The

current computer capabilities because of thenumber of the monomer unit n and polymer

enormous number of intramolecular degrees of𝑛&'( are fixed 100 and 200, respectively. All-

freedom in polymer chain. To solve thisatom molecular dynamics simulation and free-problem, we developed a chain increment

energy calculation has been computed bymethod that approaches the chemical potential

supercomputer system at ISSP. Figure 1 showsof polymers using an all-atom model [1]. The

the monomer-wise chemical potential ∆𝜇!"#$%

intermolecular interaction of a polymer ofand averaged interaction energy 〈𝑢〉! for eachinterest with the surrounding molecules ismonomer i = 20, …, and 80 by using our

introduced sequentially for the monomers

scheme [1]. It was seen that both ∆𝜇!"#$% and(segments), and the free energy for turning on〈𝑢〉!

the interaction is treated within the frameworkmonomer in the inner part of the polymer chainof

mixing.

However,360

are insensitive to the location of theActivity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyoand depends on the mixing ratio of the polymer

polymeric systems, atomistic calculations ofsystems. The standard error of ∆𝜇!"#$% at i eachsolvationof i = 20, ... , and 80 is less than ∼0.1 kcal/mol

computationallyfor both PE and PVDF at all the molar ratios inincrement

Figure 1.

approaches free energy of mixing by focusingPE∆µincr(kcal/mol)
i

1:0

1:1

1:3

0:1

are

expensive.

methodweextremely
The

have

chain

developed

monomers turn on their interactions with

−0.5
−1.0

surrounding molecules. Combined with energy-

−1.5
−2.0

representation method, the pair-wise solvation

−2.5
−3.0

<u>i (kcal/mol)energieson the solvation free energy as repeated

PVDF

3:1

free

free energy can be calculated in computational

−5.0
−6.0

time

−7.0
−8.0
−9.0

that

simulations.

20 40 60 80 20 40 60 80 20 40 60 80 20 40 60 80 20 40 60 80

isfeasiblefor

all-atomMD

Extendingthisapproachtopolymer mixtures and applying it to themonomer index i

Figure 1: Incremental free energy ∆𝝁𝐢𝐧𝐜𝐫
and the average𝒊

PE/PVDF system demonstrates that polymer

interaction energy 〈𝒖〉𝒊 against the indices i of the incremented

mutual miscibility evaluations can be computed

PE and PVDF monomers in the PE/PVDF mixture systems.

in an all-atom model.

Figure 2 shows the mixing free energy per

0.3∆mixG/N (kcal/mol)monomer ∆&") 𝐺/𝑁 of the PE/PVDF mixture
versus the molar fraction of PVDF. According
to Figure 1, ∆𝜇!"#$% of PE is within 0.1 kcal/mol
over the entire range of mixing ratios, and themole fraction dependence of

0.2

0.1

∆&") 𝐺/𝑁 is0dominated by that of PVDF. Also, the sign of0

1/4∆&") 𝐺/𝑁 is positive for each mixing ratio
examined since ∆𝜇!"#$%

1/2

3/4

1

mole fraction of PVDF

of PVDF becomes

Figure 2: Free energy of mixing per monomer ∆𝐦𝐢𝐱 𝑮/𝑵 for

negative for larger mole fractions of PVDF.

the PE/PVDF system at mole fractions of 0, 1/4, 1/2, 3/4, andFigure 2 thus shows that PE and PVDF are

1 for PVDF. Since the total number of polymer chains in theimmiscible with each other, which is consistentsystem is 200 and the degree of polymerization n is 100 for PE

with experimental reports.

and PVDF, the total number of monomers N is equal to 20000.

References
Conclusion[1] K. Yamada and N. Matubayasi: Macromol.

When solute degrees of freedom are large, as in

53 (2020) 775-788.

361

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Fugaku Battery and Fuel Cell Project
Osamu Sugino
Institute for Solid State Physics,
The University of Tokyo, Kashiwa-no-ha, Kashiwa, Chiba 277-8581
chose

Fiscal 2022 was the final year of thethosecontaining

relatively

small

Fugaku-denchi Project [1], in which weendothermic elementary reactions only. The

participated in the study of the oxygen

hydration of OH* was suggested to be the rate

reduction reaction (ORR) occurring on an oxide

determining step under the assumption of minor

electrocatalyst, i.e., zirconium oxynitride. This

contribution of the details of activation barrier.

defective zirconia is known to produce high

The corresponding reaction heat is about 0.4 eV,

voltage comparable to platinum under open

which is comparable to that on the platinum

circuit conditions, but the reaction mechanism

surface. Importantly, a similar value wasis still unknown. Due to the lack of detailed

obtained for the surface without defects,

experimental information, we have performed asuggesting that the defects play a minor role inthought experiment based on reliable first-

determining

principles simulations. We first performed a

Experimentally, however, the defects werefirst-principles Monte Carlo simulation with

reported to enhance the reaction current density.

abICS [2] to find a distribution of nitrogen

A possible explanation for the apparent

impurities (NO) and oxygen vacancies (VO).

discrepancy is that defects contribute to

The results were used to create a model where

increasing the carrier density without changing

NOs and VOs are located at a subsurface andthe activity as much. We are now examining

surface site, respectively. We then investigatedother possibilities as well by using advanced

the free-energy profile for ORR occurring on

exchange-correlation functionals, increasing thethe model [3].

number of possible structures, and consideringtheactivityof

ORR.

Since there are many possible reaction sites

the activation energies. Making a reliable

for oxide surfaces, we examined all possible

machine learning potential will be an important

adsorption structures of the ORR intermediates

step for that purpose.

and then used a quick and easy method to

We are grateful for the opportunity

determine the reaction pathways based on theprovided by the ISSP supercomputer systems to

structural similarity of the intermediates.

study the ORR intermediates in detail, which

Among the resulting free energy profiles, wewould otherwise be too challenging.

362

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

References

Vacancy on the Oxygen Reduction Reaction on[1] https://www.nims.go.jp/fugaku-

the Tetragonal Zirconia (101) Surface”, S.

denchi/en/index.html

Muhammady, J. Haruyama, S. Kasamatsu, and[2] https://www.pasums.issp.u-

O. Sugino, J. Phys. Chem. C126, 15662–15670

tokyo.ac.jp/abics/(2022).

[3] “Effect of Nitrogen Doping and Oxygen363

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Materials design of high performance magnetic materials
Tetsuya FUKUSHIMA
Institute for Solid State Physics, University of Tokyo
Kashiwa-no-ha, Kashiwa, Chiba 277-8581

In this year, we have developed an all electron first-principle calculation code, where afull-potential Korringa-Kohn-Rostoker (KKR)
Green’s function method is implemented, forthe design of permanent magnets and spintronics materials.
It is well known that the local density
approximation (LDA) and generalized gradient method (GGA) are poor approaches forstrongly correlated materials. In particular,
LDA and GGA give unrealistic results for localized f -electron systems. When the electron
configurations are not f 7 or f 14 , the occupied
and unoccupied orbitals split off due to thecorrelation energy. However, in the LDA andGGA calculations, the f orbitals do not split
and are pinned on the Fermi energy, leading
to significantly overestimated cohesive energy.
One of the origin of this problem is the error for self-interaction energy. The Coulomb
and exchange terms in a same orbital should
be canceled out, because the energy of electroninteracts with itself (self-interaction energy) iszero. This is satisfied in Hartree-Fokc approximation; however, LDA and GGA lead to finite
self-interaction energy.
We implemented the self-interaction correction (SIC) method to our full-potenital KKR
Green’ function code and applied it rare earth
compounds. In our code, the spherical electron densities of the 4f orbitals are extracted
from the Green’s function, and then the spherical components of the SIC potential are calculated. This means that the our SIC potential
is not full-potential. The full-potential SIC isneeded to accurately describe the orbital split-

(a)

(b)

(c)

(d)

-1.5

Figure 1: Calculated density of states of
NdCo5 and SmCo5 . (a,c) and (b,d) correspondto LDA and SIC results, respectively.

tings reflecting from the geometric shape of the
f orbitals. The SIC potential is constructedby using the exchange correlation potential parameterized by Moruzzi, Janak, and Williams.
In order to investigate the effect of SIC on
the electronic structure, we compared the density of states of NaCo5 and SmCo5 calculated
by LDA and SIC (see figure 1). We used theexperimental lattice constants in these calculations. The space group is P 6/mmm. As
shown in Fig. 1(a,c), in the LDA calculation,
the partially occupied f states are located on
the Fermi level. On the other hand, SIC lead tothe splitting unoccupied and occupied f states.
Note that the LDA+U method is often used tocorrect for such LDA errors, but the value ofon-site Coulomb interaction (U ) is an empirical parameter and is not suitable for materials
screening and design.
Figure 2 shows the density of states of Ce2 O3364

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Density of states (1/eV)

30
25

Ce-4f

20
15
10

O-2p

5
0
30

Density of states (1/eV)

KKR-LDA

235101 (2007)

Total

25

-6
-4
-2
0
2
4
Energy relative to the FermiTotal
level (eV)KKR-SIC

20

Ce-4f

15
10
5
0

O-2p

Ce-4f

-6
-4
-2
0
2
4
Energy relative to the Fermi level (eV)Figure 2: Density of states of Ce2 O3 calculated
by LDA and SIC.
calculated by LDA and SIC. The space groupis P¯3m1 and the antiferromagnetic state is
assumed in the calculations. It is thoughtthat the valence of the Ce ion in Ce2 O3 is
3+ and the electron configuration f 1 is realized. In the SIC calculation, the occupied fstates containing one electron are pushed down
to energetically lower region, and are locatedaround -3.0 eV. The unoccupied f states lie
around 1.0 eV to 3.0 eV. Pourovskii et al. performed the electronic structure calculations for
Ce2 O3 on the basis of Linear Muffin-Tin Orbital Method with dynamical mean field approximation (DMFT) [1]. Our results obtained
from the SIC calculations are quite consistent
with their DMFT calculations.

References[1] L. V. Pourovskii, B. Amadon, S. Biermann, and A. Georges, Phys. Rev. B 76,

365

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Development of high-performance permanent
magnets by large-scale simulation and data-driven
approach
Takashi MIYAKE
CD-FMat, National Institute of Advanced Industrial Science and Technology
Umezono, Tsukuba, Ibaraki 305-8568

The calculation is based on density functional theory in the local density approximation. We use AkaiKKR which is based on
the Korringa-Kohn-Rostoker Green function
method. The open-core approximation is used
for the f electrons in Nd and Sm. Randomness caused by doping is treated by coherent
potential approximation.
In order to investigate the stability, we
consider R1−ζ1 −ζ2 −ζ3 −ζ4 Zrζ1 Feζ2 Coζ3 Tiζ4 . In
the four-dimensional space (ζ1 , ζ2 , ζ3 , ζ4 ), we
compare the total energy for the 1-12, 217, and unary phases, and identified stablephases. Figure 1 shows the phase diagramof the Sm system on the (Sm,Zr)(Fe,Co)12
plane. We then compare the total energy difference between (R1−α Zrα )(Fe1−β Coβ )12−γ
Tiγ and stable phases, and calculate thehull distance. Figure 2 shows the result for(Sm1−α Zrα )(Fe1−β Coβ )12 . We see that thehull distance is not monotonic as a functionof α and β. We also see that the hull distanceis small for high Zr concentration.
1.0 (1)0.8
Zr concentration

RFe12 -type compounds with the ThMn12
structure are potential main phase for rareearth magnets. Because of high Fe content,high magnetization and high Curie temperature can be expected. However, RFe12 is
known to be unstable, and there have been
many trials for stabilizing the ThMn12 structure by chemical doping. In this study, weinvestigate the stability of (R,Zr)(Fe,Co,Ti)12(R=Y, Nd, Sm) against the 2-17 and unaryphases by first-principles calculation [1].

(4)

(5)

(6)

0.6
0.4
0.2
0.0
0.0

(2)

(7)

(8)

(3)

0.2

0.4
0.6
Co concentration

0.8

1.0

Figure 1:
Phase diagram on the(Sm,Zr)(Fe,Co)12 plane.
(1) (S,Zr)Fe12 ,Zr(Fe,Co)12 , Zr2 Fe17 and unary Fe; (2)(Sm,Zr)Fe12 , Sm2 (Fe,Co)17 , and unary Sm
and Fe; (3) (Sm,Zr)Fe12 , Sm2 (Fe,Co)17 , andunary Fe; (4) (Sm,Zr)Fe12 , Zr(Fe,Co)12 ,Sm2 (Fe,Co)17 , and unary Fe; (5) Zr(Fe,Co)12 ,Sm2 (Fe,Co)17 , and unary Fe; (6) Zr(Fe,Co)12 ,Sm2 (Fe,Co)17 , Zr2 Co17 , and unary Fe; (7)Sm2 (Fe,Co)17 , Zr2 Co17 , and unary Fe; (8)Sm2 Co17 , Zr2 Co17 , and unary Fe and Co.

References[1] Taro Fukazawa, Yosuke Harashima,
Hisazumi Akai and Takashi Miyake:Phys. Rev. Mater. 6, 054404 (2022).

366

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

0.030

1.0

Zr concentration

0.025

0.010

0.8

0.020

0.6

0.015
0.4

0.0
3

0

0.005

40

0.0
0.0

0.010

20

0.0

0.2

0.
0

0.2

0.4
0.6
Co concentration

0.8

1.0

0.000

Figure 2:
Hull distance in eV for(Sm1−α Zrα )(Fe1−β Coβ )12 with the ThMn12
structure.

367

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Data driven analysis for impurity eﬀects on
photocatalysts
Mikiya FUJII and Yosuke HARASHIMADivision of Materials Science, Nara Institute of Science and Technology
Takayama-cho, Ikoma, Nara 630-0192

Photocatalytic generation of hydrogen fromsolar energy (green hydrogen) is one of the key
elements for solving resource, energy, and environmental problems. The purpose of thisstudy is to obtain guidelines for controlling theproperties of new photocatalytic compounds
and impurities to improve the yield of hydrogen production in response to visible light,
which is still not suﬃcient. For this purpose, a combined analysis of ﬁrst-principles
calculations and informatics will be performed.
While materials development has been conducted within a limited search space basedon experience, this study will develop a newresearch in photocatalytic materials development by utilizing information science to predict and develop new materials with a greatly
expanded search space. [1, 2] In addition tosolving energy problems through hydrogenproducing photocatalysts, this research will
have a spillover eﬀect on the synthesis ofcarbon-neutral petrochemical feedstocks from
CO2 and green hydrogen.
Process informatics for calcination processwith dynamical Monte Carlo simulations:
The hydrogen evolution rate (HER) of photocatalytic materials depends strongly on theircalcination processes. The calcination process will determine an amount of product compound, defect, and surface area. The HER
is considered to be highly dependent on thosefeatures, however, experimental analysis to detect the features is time-consuming. We developed a dynamical Monte Carlo (DMC) simu-

lation code to predict the features, and constructed an HER prediction model using the
calculated features. The DMC code is written in C++ and the HER prediction code isin Python. The DMC code is based on formation energies of product compounds, defectenergies, and surface energies. We will use
the ﬁrst-principles calculation code, Vienna Ab
initio Simulation Package (VASP), to calculatethese quantities. In Ref. [3], quantum chemical
calculations were used as explanatory variablesin the model to predict experimental values in
ﬂow copolymerization [4]. It was found that
information on the so-called transition statesof intermediates is important in predicting the
products of chemical reactions. This is a kindof data assimilation [5], which we will incorporate into our future method of calculatingintermediates in crystal formation by processsimulation.
Highthroughput search of high entropy catalyst:
Platinum is known for its high catalytic performance, but it is very rare, so there is aneed to develop alternatives. We performed asystematic search for high-entropy alloys thatwould mimic platinum. The electronic structures were calculated by using ﬁrst-principles
code, AkaiKKR, which is based on the KKRGreen’s function method. It enables us toconsider nonstoichiometric chemical compositions by the coherent potential approximation. We considered up to a quaternary system
which consist of transition metal elements andperformed ﬁrst-principles calculations on more

368

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

than 100,000 samples.

References[1] M. Fujii: JSAP Review, 2022, 220416(2022).
[2] 藤井幹也: 応用物理, 91, 688 (2022).
[3] S. Takasuka, et al.: Digital Discovery, accepted.
[4] A. Wakiuchi, et al.: Macromol. Mater.
Eng. 2200626 (2022).
[5] Y. Harashima: (invited talk) The 24th
Annual Meeting of Japan Society of Theoretical Chemistry, May 18th 2023.

369

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Computational and Data Materials Science Study for ET
Revolution by Developing Next-Generation Battery and
Fuel Cell
Yoshitaka TATEYAMANational Institute for Materials Science,
Namiki 1-1, Tsukuba, Ibaraki 305-0044
We clarified a long-standing problem of

We further addressed the speed-up of theatomistic insight of the dopant effect on the ionic

conductivity calculations by developing a new

conduction around grain boundary, by extensive

method of chemical charge diffusion non-first-principles MD calculations of diffusion

equilibriumcoefficients[1].

demonstrated that this new method surely

MD(CCD-NEMD),

andaccelerates the sampling of mean-squaredisplacement[3].

Fig. 1: Calculated self-diffusion coefficients of
Li7La3Zr2O12 solid electrolyte.
Fig. 3: (left) Definition of flux and color
charges to the Li ions and anion units in the
Li10GeP2S12 model system. (right) Schematic
picture about the speed-up of the evaluation ofcorrelated ionic conductivity.

We also developed a high-throughput firstprinciples MD sampling flow for the ionic
conductivity

calculations,andproposedpotential solid electrolytes with higher ionic

References

conducitivy by applying the flow to Na-ion[1] B. Gao, R. Jalem, and Y. Tateyama, J. Mater.

sulfide solid electrolytes[2].

Chem. A, 10, 10083-10091 (2022).
[2] S.-H. Jang, Y. Tateyama, and R. Jalem, Adv.
Funct. Mater. 32, 2206036 (2022).
[3] R. Sasaki, B. Gao, T. Hitosugi, and Y.

Fig. 2: Schematic picture of the highthroughput screening flow for the search forsolid electrolyte materials with the higher ionic
conductivities.

370

Tateyama, npj Comput. Mater. 9, 48 (2023).

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Theoretical study of hydrogen-evolution
semiconductor photocatalysts using first-principles
calculations
Seiichiro L. TEN-NO
Graduate School of System Informatics, Kobe University
Rokkodai-Cho, Nada-Ku, Kobe 657-8501

Planar Defect in Layered Perovskite
Photocatalyst Y2 Ti2 O5 S2
Layered perovskite Y2 Ti2 O5 S2 (YTOS) isa promising semiconductor photocatalyst with
an electronic structure suitable for overall water splitting under visible light. Similar toother photocatalysts, structural defects during
synthesis should be controlled to promote photocatalytic performance [1, 2]. Very recently,
transmission electron microscopy (TEM) haverevealed extremely large planar defects composed of S–Mg–S layers in YTOS synthesizedusing the flux method. It is not clear how sucha characteristic planar defect makes an eﬀect
on photocatalytic activities in YTOS.
Motivated by this observation, we investigated the planar defect structure and electronic structure using first-principles calculations based on the density functional theory(DFT) [3]. The DFT calculations were performed within the PBE+U (Perdew–Burke–
Ernzerhof functional with Hubbard U correction) exchange-correlation functional using the
projector augmented wave (PAW) method, as
implemented in the Vienna ab initio simulation package (VASP). Here we set U = 7.5 eV
for the 3d orbitals at the Ti sites to reproduce
the experimental band gap, 1.9–2.0 eV [1].
We have microscopically determined the planar defect structure using the structural optimization, where a Mg atom is centered in
the ab-square intersecting the defect S layers(Fig. 1). The obtained defect structure is consistent with the ADF–STEM image: for example, the distance between the Y layers of thedefect model, 9.93 Å, is in quantitative agreement with the experimental value, ca. 10.00
Å.
(b)

Y layerr

Y
Ti
O
S

(a)
Defect-S

9.93 Å

Defect-Mg
Defect-S

Y layerMg(001)

Figure 1: The optimized structure of (a) the
conventional unit cell and (b) supercell with
the MgS2 planar defect.
To understand the electronic structures, wehave investigated the projected density ofstates (PDOS) and band structure shown in
Fig. 2. The electronic structure shows that the371

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

defect bands appear within the original bandgap. The large dispersion of the bands reflects
the planar (two-dimensional) nature of the defect in S and Mg with a high state density,
in contrast to point defects. It is expectedthat such defect bands considerably increase
the electron–hole recombination rate, enhancing the interband transitions of the defect valence and/or defect conduction bands via radiative or nonradiative transitions, which is anundesirable factor for the eﬃciency of the overall water splitting of YTOS.

Mg-poorPDOS (states/eV)

60
50

evant compounds in the synthesis of YTOS.
Fig. 3 shows Ef as a function of the chemical potential of S relative to bulk precipitate(∆µS ), where A, B, and C denote the representive points of the phase diagram. The evaluation of the formation energy suggests that Ef
increases with decreasing ∆µS and/or ∆µMg .
Therefore, the optimum conditions for the S
and Mg environments must be optimized forthe synthesis of YTOS,which suppresses theformation of the planar defect.

Total
Y−4d
Ti−3d

(a)

O−2p
S−3p
Mg−3s

40

Mg-rich30
Defectlevels20

S-poor

S-rich

10
0
−6

−4

−2

0

2

4

6

E−EV (eV)Figure 3: Formation energy of the MgS2 planar defect Ef against ∆µS . The red, blue, andyellow lines represent Ef at points A, B, andC of the phase diagram, respectively.

6(b)E−EV (eV)

4
2
0
−2

References

−4
−6
Γ

X

M

ΓZ

R

A

Z

k−pathFigure 2: (a) PDOS for YTOS with the planar defect. (b) Band structure with the planar
defect. The defect S-3px,y , S-3pz , and Mg-3s
weights are colored in cyan, purple, and orange, respectively. The energy (E) for eachcase is measured from the valence band maximum (EV ).

[1] Q. Wang et. al.: Nat. Mater. 18, 827(2019).
[2] Z. Pan et. al.: Res. Chem. Intermed. 47,
225 (2021).
[3] M. Nakabayashi, K. Nishiguchi, X. Liang,
T. Hisatomi, T. Takata, T. Tsuchimochi, N. Shibata, K. Domen, and S.
L. Ten-no: J. Phys. Chem. C (in press:
https://doi.org/10.1021/acs.jpcc.3c00820).

To consider the stability of the planar defect,
we have finally evaluated the defect formationenergy (Ef ). The chemical potentials µi were
determined by the phase diagram of the rel-372

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

3.6

Software Advancement Projects
and Workshop Support373

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Acceleration of first-principles statistical thermodynamics
framework abICS using an on-lattice neural network modeland active learning
Shusuke KASAMATSU1, Tatsumi AOYAMA2, Yuichi MOTOYAMA2,
Kazuyoshi YOSHIMI2, and Osamu SUGINO2
1

Faculty of Science, Academic Assembly, Yamagata University,
Yamagata 990-85602

Institute for Solid State Physics, University of Tokyo, Kashiwa 277-8581
which we outline in this report.

In 2019, ab Initio Configuration Sampling

The abICS framework developed in thistoolkit, or abICS for short, was developed as anforproject uses Python as a glue language to piece

directly combining first-principles relaxation

together the necessary components (Fig. 1). It

and total energy calculations with replica

is registered on PyPI (Python Package Index;

exchange Monte Carlo (RXMC) sampling [1].

pypi.org) and can be installed easily in most

The motivation was that effective models suchenvironments through the command “pipas cluster expansion, which are used to speed

install --user abics”. A typical use caseup the usual Metropolis Monte Carlo samplingis outlined as follows:

for analyzing order/disorder in alloy systems,

1.

ISSP Software

Advancement

Project

Random configurations on a lattice areare limited in describing complex many-

relaxed and their total energies arecomponent systems with sufficient accuracy.

calculated using the first-principles code

Our aim at that time was to enable directof choice. We support VASP, Quantum

sampling

Espresso, and OpenMX.

on

first-principles

energiesby2.

employing highly parallel sampling methods

A neural network model is trained on datasuch as RXMC in combination with massively

from step 1 to predict relaxed energies

parallel supercomputing resources. A few years

from configurations on the non-relaxedlater, some of the authors found that an on-

lattice. We currently use aenet code [4] for

lattice neural network model overcomes many

the neural network training.
3.

issues found in previous effective models if

RXMC or population annealing MC

sufficient training data is provided in an active(PAMC) calculation is performed using

learning setting [2,3]. This led to the proposal

the neural network model from step 2.

of last year’s Software Advancement Project,

Two sampling modes are available:

374

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

canonical sampling with a fixed number

separate files following formats of the specified

of atoms or grand canonical samplingsolver.

the

We believe that abICS will be a game-composition. For the neural networkchanger in modeling order/disorder in many-

evaluation, we provide a file IO-based

component crystalline systems as we haveinterface to aenet, and we also provide analready demonstrated for partially hydrated Sc-

interface to aenet-lammps [5] python

doped BaZrO3 [3]. Please do not refrain frominterface which does not rely on file IO

contacting us if you find any difficulties inand is thus usually faster.

using or extending this software.

which

4.

allowsforchangesin

A subset of samples from step 3 arerelaxed and their total energies are

References

calculated

using[1] S. Kasamatsu and O. Sugino, J. Phys.:calculation.

If

the

first-principles
results

Condens. Matter 31, 085901 (2019).

deviateconsiderably from the neural network[2] S. Kasamatsu et al., J. Chem. Phys. 157,

prediction, the data is added to the training

104114 (2022).

data set and the procedure is repeated[3] K. Hoshino, S. Kasamatsu, et al., Chem.

from step 2.

Mater. 35, 2289-2301 (2023).
[4] N. Artrith et al., Phys. Rev. B 96, 014112

The overall procedure is controlled by an(2017). http://ann.atomistic.net/

input file in TOML format, which is an easy-to-[5] M. S. Chen et al., J. Chem. Phys. 155,

read software configuration format that is being

074801 (2021). https://github.com/HidekiMori-

used in many projects [6]. The parameters for

CIT/aenet-lammps

the first-principles calculations and neural[6] https://github.com/toml-lang/toml

network training/evaluation are controlled by

Fig. 1: Overview of the active learning procedure using abICS.

375

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

H-wave: A quantum lattice solver using
Wavenumber-Space Mean Field Approximation andRandom Phase Approximation
Akito Kobayashia , Tatsumi Aoyamab , Yuichi Motoyamab , Takeo Katob , and Kazuyoshi YoshimibaDepartment of Physics, Nagoya University, Nagoya 464-8602
bInstitute for Solid State Physics, University of Tokyo, Kashiwa-no-ha, Kashiwa, Chiba 277-8581

In a project for Advancement of Software
Usability in Materials Science (PASUMS) [1]in FY2022, H-wave [2] has been developed toperform unrestricted Hartree-Fock (UHF) approximation in wavenumber space and random
phase approximation (RPA) for itinerant electron systems. UHF and RPA correspond tosimple approximations that deal with fluctuations up to first order and enable analysesof electron correlation effects in materials at
a low computational cost. The input files describing the one-body and two-body interactions are based on the Wannier90 [3] format.
This allows smooth connection for the software packages that derive the effective models from first principles calculations, such as
RESPACK [4], to the analyses of the effectivemodel with H-wave.
H-wave is implemented in Python3 andis available from PyPI package repository.
The source archive can be obtained from theGitHub site1 . To use H-wave, we must prepare an input parameter file written in the
TOML format which specifies calculation conditions and file names of the definition files forthe geometry and Hamiltonian. The Hamiltonian is defined through the input files of
the transfer integrals and the interactions denoted by CoulombIntra, CoulombInter, Hund,
Ising, Exchange, and PairLift. In UHF mode,a PairHop term can also be specified. To learn
1

https://github.com/issp-center-dev/h-wave

Figure 1: A logo of H-wave designed by T.
Kawamura.
details of the input files, the readers may referto the manual [2]. Once these input files areprepared, the calculation will be performed bytyping the following command:$ hwave input.toml
where hwave is the executable file of H-wave
and input.toml is the name of the input file.
After the calculations are completed, the following physical quantities are obtained:
in UHF mode: energy, eigen values and vectors, and one-body Green’s functions.
in RPA mode: irreducible susceptibilities
A brief tutorial is included in the manual,and several application examples have been
uploaded to the ISSP data repository [5]. For
the UHF calculations, we provide examples
such as the band and density of state plots,
and the finite temperature calculations. In addition, a more applied example analysis of the376

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Figure 2: The band dispersion of SSH model.
Su–Schrieffer-Heeger (SSH) model [6] which isone of the well-known platforms for the emergence of the topological insulator is also provided. In this sample, the scripts to obtain theband dispersion (shown in Fig.2) and the Zakphase [7] of the lowest band, which corresponds
to the topological number, are also provided.
For RPA calculations, we provide examples forcalculating charge and spin susceptibility that
reproduce the results of Refs. [8, 9]. Figure 3
shows the static charge and spin susceptibilities (χs (q, 0) and χc (q, 0)) for one-orbit twodimensional extended Hubbard model on thesquare lattice at (U, V ) = (3.7, 0) and (0, 0.8)
examined in Ref. [8].
In this report, we introduced the featuresand examples of H-wave developed throughPASUMS. In the future, we would like to develop functions such as follows; (a) providing
calculation scripts for quantities corresponding to dynamic susceptibility measured in experiments, (b) evaluating the instability of superconducting transition by solving the linear Eliashberg equation considering charge andspin fluctuations as pairing interactions, and(c) adding more samples such as the calculation with spin-orbit interactions. We believethat H-wave is useful for a wide range of materials scientists and can contribute to the promotion of the computational materials design.
We would like to acknowledge K. Ido, T.

Figure 3: The charge and spin susceptibilities
at (U, V ) = (3.7, 0) and (0, 0.8) in Ref. [8].
Misawa, and T. Kawamura for their contribution to code development and useful discussion. AK and KY were supported by JSPS
KAKENHI Grant Number 22K03526. KY wassupported by JSPS KAKENHI Grant Number
21H01041.

References[1] https://www.pasums.issp.u-tokyo.ac.jp[2] https://www.pasums.issp.u-tokyo.ac.jp/hwave[3] G. Pizzi et al, J. Phys.: Condens. Matter
32 165902 (2020).
[4] K. Nakmura, Y. Yoshimoto, Y. Nomura etal., Comp. Phys. Commun. 261, 107781(2021).
[5] https://isspns-gitlab.issp.utokyo.ac.jp/hwave-dev/hwave-gallery[6] W. P. Su, J. R. Schrieffer, and A. J. Heeger,
Phys. Rev. Lett. 42, 1698 (1979).
[7] J. Zak, Phys. Rev. Lett. 62, 2747 (1989).
[8] A. Kobayashi, Y. Tanaka, M. Ogata, and
Y. Suzumura, J. Phys. Soc. Jpn. 73, 3135
(2004).
[9] K. Yoshimi, M. Nakamura, and H. Mori, J.
Phys. Soc. Jpn. 76, 024706 (2007).

377

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Report of CCMS hands-on sessions in the 2022 fiscal year
Kota IDO
Institute for Solid State Physics,
The University of Tokyo, Kashiwa-no-ha, Kashiwa, Chiba 277-8581
In the 2022 fiscal year, Center for

Features of each software are as follows.

Computational Materials Science (CCMS) in the

2DMAT is a Python package to search an

Institute for Solid State Physics (ISSP) held 8

optimal result by applying a search algorithm tohands-ona direct-problem solver [10]. abICS is an absessions

where

the

ISSP

supercomputer was used [1-8]. In this report, we

initiobriefly summarize them.

configuration samplings in disordered systems

softwareframeworktoperform

Table 1 shows the list of the hands-on[11]. PHYSBO is a Python library to perform asessions in the 2022 fiscal year. There are 8

fast and scalable Bayesian optimization [12].

software used in the hands-on sessions: 2DMAT,TeNeS is a software package for performing

abICS, PHYSBO, TeNeS, HΦ, mVMC, DSQSS,

simulations in quantum many-body systems

and ESM-RISM. Development of the software

based on tensor network methods [13]. HΦ is amentioned above was supported by “Project for

package of solvers for quantum many-bodyadvancement of software usability in materials

systems based on exact diagonalization methods

science” (PASUMS) [9].

[14]. mVMC is a low-energy solver based on thevariational Monte Carlo method for a wide range

Table 1: List of CCMS hands-on sessions whereof quantum lattice models [15]. DSQSS is a

the ISSP supercomputer was used in the 2022

software package of the quantum Monte Carlofiscal year.

method based on Feynman’s path integral

Date
Apr. 26
June 27
June 20

LecturerK. Yoshimi et al.
S. Kasamatsu et al.
K. Tsuda et al.

Oct. 25
Nov. 21
Nov. 28
Dec. 14
Dec. 22

T. Okubo et al.
Y. Yamaji et al.
T. Misawa et al.
Y. Motoyama et al.
M. Ohtani et al.

Software
2DMAT[10]abICS[11]PHYSBO[12]
TeNeS[13]HΦ[14]
mVMC[15]DSQSS[16]
ESMRISM[17]representation for quantum lattice systems [16].
ESM-RISM is an ab initio method forelectrode/electrolyte interfaces, which has been
implemented in open-source software Quantum
ESPRESSO [17, 18].
In all of the hands-on sessions, lecturers
explained the basics of each software and gave
its tutorial. Materials of some hands-on sessions
are available on each official page.

378

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

References[1] https://ccms.issp.u-tokyo.ac.jp/event/5274[2] https://ccms.issp.u-tokyo.ac.jp/event/5099[3] https://ccms.issp.u-tokyo.ac.jp/event/5456[4] https://ccms.issp.u-tokyo.ac.jp/event/5631[5] https://ccms.issp.u-tokyo.ac.jp/event/5659[6] https://ccms.issp.u-tokyo.ac.jp/event/5665[7] https://ccms.issp.u-tokyo.ac.jp/event/5685[8] https://ccms.issp.u-tokyo.ac.jp/event/5698[9] https://www.pasums.issp.u-tokyo.ac.jp[10] https://www.pasums.issp.utokyo.ac.jp/2dmat/en/[11] https://www.pasums.issp.utokyo.ac.jp/abics/en/[12] https://www.pasums.issp.utokyo.ac.jp/physbo/en/[13] https://www.pasums.issp.utokyo.ac.jp/tenes/en[14] https://www.pasums.issp.utokyo.ac.jp/hphi/en/[15] https://www.pasums.issp.utokyo.ac.jp/mvmc/en/[16] https://www.pasums.issp.utokyo.ac.jp/dsqss/en/[17] https://www2.ccs.tsukuba.ac.jp/public/
otani/programs.html[18] https://www.quantum-espresso.org

379

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Supercomputer course of Computational Materials Design(CMD®) workshop
Masaaki GESHI1, Yoshitada MORIKAWA2, Tomoya ONO3
1 3

R Institute for Newly-Emerging Science Design,

Osaka University, Machikaneyama, Toyonaka, Osaka 560-0043
2

Department of Precision Engineering,Osaka University, Yamada-oka, Suita, Osaka 565-0871
3

Graduate School of Engineering,

Kobe University, Rokkodai-cho, Nada, Kobe 657-8501
The 41st Computational Materials Design

catalytic surfaces, reactions at solid-solid(CMD®) workshop (CMD41) has been heldinterfaces, and so on.

from September 5th to September 9th and theperformed calculations and examined their

42nd CMD® workshop (CMD42) has been done

results.

from February 20th to February 24th. Both were

The participants

In CMD42 three participants took theheld online. In this workshop we have thesupercomputersupercomputer course to train up humansupercomputer of ISSP. They got a tutorial on

resources to advance research by using system B

RSPACE developed by T. Ono. After giving thesupercomputer of ISSP, the University of Tokyo.

introductory lecture of large-scale computing by

In CMD41, nine participants took the

M. Geshi and introducing the calculation

supercomputer course and followed a tutorial on

method of electronic structures and electron

STATE-Senri developed by Y. Morikawa. After

conduction property using RSPACE, exercises

an introductory lecture on large-scale computing

instructed in the textbook were carried out. Then,and an explanation of how to use theelectronic structure calculations were carried out

supercomputer of ISSP by M. Geshi, thefor various molecules, and the electronic density

participants started constructing computational

distribution was visualized. The attendees alsomodels for their own research subjects with theperformed massively parallel calculations to

help of the lecturers. Then they carried out

confirm the efficiency for parallel computing.

calculations

Finally, the electron conduction properties ofusing supercomputers. Specificcourseandthemes included molecular adsorption on solid

two-dimensionalsurfaces, chemical reactions of molecules inmaterials

solutions, chemical reactions of molecules onelectrodes were investigated.

380

materials

sandwiched

used

the

and

dialectic

betweenmetallic

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

4

PUBLICATION LIST

381

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Example:LASTNAME, Firstname [ project class; # points (B), # points (C) ] (Page #)— Project title
1. First paper
Names of Authors, etc.
2. Second paper...

2 ISSP Joint Research Projects◦ B–E classes
ADACHI, Kyosuke [ B class; 700 (B), 100 (C) ] (311)— Multi-component phase separation dynamics of proteins
AKASHI, Ryosuke [ B class; 600 (B), 100 (C) ] (312)— Solution of the Eliashberg equation with the electron-electron Coulomb interaction
1. Revisiting homogeneous electron gas in pursuit of properly normed ab initio Eliashberg theory
R. Akashi Physical Review B 105, 104510 (2022).
DOI:10.1103/PhysRevB.105.104510

AOYAMA, Kazushi [ B class; 900 (B), 180 (C) ] (291)— Magnetic ﬁeld eﬀect on a topological chiral order in breathing-kagome antiferromagnets— Topological chirality order and its stability in breathing-kagome antiferromagnets
1. Zero-Field Miniature Skyrmion Crystal and Chiral Domain State in Breathing-Kagome Antiferromagnets
K. Aoyama and H. Kawamura, J. Phys. Soc. Jpn. 92, (2023) 033701.
DOI:10.7566/JPSJ.92.033701
2.

Spin and thermal transport and critical phenomena in three-dimensional antiferromagnets
K. Aoyama, Phys. Rev. B 106, (2022) 224407.
DOI:10.1103/PhysRevB.106.224407

3.

Hedgehog lattice and ﬁeld-induced chirality in breathing-pyrochlore Heisenberg antiferromagnets
K. Aoyama and H. Kawamura, Phys. Rev. B 106, (2022) 064412.
DOI:10.1103/PhysRevB.106.064412

ARAI, Munehito [ C class; 6600 (B), 0 (C) ] (230)— Theoretical design of novel antibodies that inhibit disease-related protein-protein interactions— Theoretical design of novel proteins for medical and industrial applications
ARAI, Toyoko [ B class; 500 (B), 80 (C) ] (170)— RSDFT calculation of the force acting between a tip and a sample detected by noncontact atomic
force microscope382

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

ARAKI, Takeaki [ B class; 900 (B), 170 (C) ] (293, 294)
— Aggregation of polyelectrolytes in mixed solvents— Molecular modeling of ferroelectric nematic phase
ARIMA, Kenta [ B class; 700 (B), 170 (C) ] (305)— Calculation of electronic structures of graphene nanoribbons with wrinkles— Understanding electronic structures and reactivity of functional graphene sheets toward machining
catalyst
ARUGA, Tetsuya [ B class; 500 (B), 160 (C) ] (160, 162)
— In-plane interaction of conjugated molecules— Interaction of magnetic metal phthalocyanines with metal surfaces
BIN, Xu [ C class; 3400 (B), 400 (C) ] (108)— Machine learning based optimized polymer for thermal function materials
BUERKLE, Marius [ B,C class; 5600 (B), 550 (C) ] ()— Combining ﬁrst-principles quantum transport and deep learning to create a virtual experimental environment— First-principles computational study of nanocrystals for photovoltaic applications
BUI, VANPHO [ C class; 1400 (B), 0 (C) ] (130)— Study on the catalytic mechanism of RuTi alloy in catalyst referred etching method
DEKURA, Shun [ C,D class; 5200 (B), 640 (C) ] (79, 80, 81)— Elucidation of the mechanism of fast proton conduction based on proton tautomerism in molecular
crystals by using ﬁrst-principles calculations— First-principles calculations of electron correlation parameters to elucidate
pi-electron-proton coupled physical properties of the unconventional molecular conductors Cat-TTF analogues— Systematic elucidation of structure and electronic state of
pi-electron-proton coupled molecular conductors under pressure
1. Conjugation Length Eﬀect on the Conducting Behavior of Single-crystalline Oligo(3,4-ethylenedioxythiophene)(nEDOT) Radical Cation Salts
R. Kameyama, T. Fujino, S. Dekura, H. Mori, Phys. Chem. Chem. Phys. 24, 9130 (2022).
DOI:10.1039/D2CP00250G
2.

Band-ﬁlling eﬀects in single-crystalline oligomer models for doped PEDOT: 3,4-ethylenedioxythiophene(EDOT) dimer salt with hydrogen-bonded inﬁnite sulfate anion chains
R. Kameyama, T. Fujino, S. Dekura, S. Imajo, T. Miyamoto, H. Okamoto, H. Mori, J. Mater.
Chem. C 10, 7543 (2022).
DOI:10.1039/D2TC01216B

3.

Molecular Arrangement Control of [1]Benzothieno[3,2-b][1]benzothiophene (BTBT) via ChargeAssisted Hydrogen Bond
R. Akai, K. Oka, S. Dekura, H. Mori, N. Tohnai, Bull. Chem. Soc. Jpn. 95, 1178 (2022).
DOI:10.1246/bcsj.20220134

4.

Neutral Radical Molecular Conductors Based on a Gold Dimethoxybenzenedithiolene Complex
with and without Crystal Solvent
S. Yokomori, S. Dekura, A. Ueda, T. Higashino, H. Mori, Chem. Lett. 52, 25 (2023).
DOI:10.1246/cl.220446

383

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

EGAMI, Yoshiyuki [ C class; 11400 (B), 950 (C) ] (55)
— First-principles electron-transport study on 2D layered material-based heterostructures— First-principles study on electron-transport properties of large scale atomic layered materials
1. Unique Electrical Signature of Phosphate for Speciﬁc Single-Molecule Detection of Peptide Phosphorylation
T. Harashima, Y. Egami, K. Homma, Y. Jono, S. Kaneko, S. Fujii, T. Ono, and T. Nishino, J.
Am. Chem. Soc. 144, 17449 (2022).
DOI:10.1021/jacs.2c05787

FUCHIZAKI, Kazuhiro [ C class; 2600 (B), 0 (C) ] (269)— Kinetics of phase transition and polyamorphism
1. Can weight hysteresis in a neural network judge the continuity/discontinuity of a phase transition?
K. Nakamura and K. Fuchizaki, submitted to J. Phys. A.

FUJI, Yohei [ B class; 700 (B), 180 (C) ] (303)— Numerical investigation of universality classes in measurement-induced phase transitions
1. Charge ﬂuctuation and charge-resolved entanglement in a monitored quantum circuit with U (1)symmetry
H. Oshima and Y. Fuji, Phys. Rev. B 107, 014308 (2023).
DOI:10.1103/PhysRevB.107.014308

FUJII, Susumu [ C class; 2800 (B), 0 (C) ] (267)— Systematic searches for grain boundary structures and structure-based prediction of multiple properties
FUJIMOTO, Yoshitaka [ C class; 600 (B), 0 (C) ] (164)— Atomic structures and electronic properties of nanocarbon-based materials
1. Theoretical study on quantum transport of carbon nanotubes for detecting toxic molecules: The
role of dopants
Y. Fujimoto, Journal of Electrochemical Science and Engineering 12, 431 (2022).
2.

First-Principles Theoretical Design of Graphene-Based Field Eﬀect Transistors
Y. Fujimoto, Advanced Nanoscale MOSFET architectures: Current Trends and Future Perspectives, Accepted.

FUJINO, Tomoko [ C class; 1200 (B), 0 (C) ] (204)— Electronic structures of EDXT oligomers in charge transfer salts: estimation of intramolecular andintermolecular Coulomb repulsion energy
1. Ambipolar Nickel Dithiolene Complex Semiconductors: from One- to Two-dimensional Electronic
Structures based upon Alkoxy Chain Lengths
M. Ito, T. Fujino*, L. Zhang, S. Yokomori, T. Higashino, R. Makiura, K. J. Takeno, T. Ozaki, H.
Mori*, J. Am. Chem. Soc. 145, 2127 (2023).
DOI:10.1021/jacs.2c08015
2.

Band-ﬁlling Eﬀects in Single-crystalline Oligomer Models for Doped PEDOT: 3,4-Ethylenedioxythiophene(EDOT) Dimer Salt with Hydrogen-bonded Inﬁnite Sulfate Anion Chains
R. Kameyama, T. Fujino*, S. Dekura, S. Imajo, T. Miyamoto, H. Okamoto, H. Mori*, J. Mater.
Chem. C 10, 7543 (2022).
DOI:10.1039/D2TC01216B

384

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

FUJISHIRO, Hiroki [ C class; 2800 (B), 0 (C) ] ()— Strained Band-Structure Engineering for Antimonide-Based Terahertz Transistors
FUKUDA, Jun-ichi [ B class; 800 (B), 80 (C) ] (302)— Calculation of ordered structures dynamics and optical properties of soft materials
1. Simulation of a cholesteric blue phase cell with large but ﬁnite thickness
J. Fukuda, Front. Soft Matter 2, 1011618 (2022).
DOI:10.3389/frsfm.2022.1011618

FUKUDA, Masahiro [ B class; 400 (B), 80 (C) ] (176)— AB type 2D materials database construction by DFT
1. Prediction of quaternary hydrides based on densest ternary sphere packings
Ryotaro Koshoji, Masahiro Fukuda, Mitsuaki Kawamura, and Taisuke Ozaki, Phys. Rev. Materials 6, 114802 (2022).
DOI:10.1103/PhysRevMaterials.6.114802
2.

Atomic arrangement of Si adatom on the Silicene/Ag(111) surface
Yuuki Adachi, Zhang Runnan, Wang Xinbo, Masahiro Fukuda, Taisuke Ozaki, Yoshiaki Sugimoto,Applied Surface Science, 157336 (2023).
DOI:10.1016/j.apsusc.2023.157336

Data Repository
OpenMX database of Atoms using standard basis sets
https://isspns-gitlab.issp.u-tokyo.ac.jp/masahiro.fukuda/openmx-database-of-atoms-using-standard-basissets
OpenMX database of bulks using standard basis set
https://isspns-gitlab.issp.u-tokyo.ac.jp/masahiro.fukuda/openmx-database-of-bulks-using-standard-basisset
Structure map of AB2 type 2D materials by high-throughput DFT calculations
https://isspns-gitlab.issp.u-tokyo.ac.jp/masahiro.fukuda/structure-map-of-ab2-type-2d-materials-by-highthroughput-dft-calculations
DOI:10.1039/D0MA00999G

FUKUDA, Tuneo [ C class; 400 (B), 0 (C) ] (184)— First-principles study of stability of Cu-Ni two-dimensional alloy
FUKUMOTO, Yoshiyuki [ B class; 800 (B), 0 (C) ] (306, 308)
— Numerical studies on thermodynamics and excitation spectra in Heisenberg antiferromagnets on thetriangular and kagome lattices— Numerical study of the eﬀect of large bond randomness on the magnetization process and magnetic
susceptibility in the spherical Kagome system {W72V30}
1. Eﬀects of bond-randomness and Dzyaloshinskii-Moriya interactions on the speciﬁc heat at low
temperatures of a spherical kagome cluster in {W72 V30 }M. Motohashi, K. Inoue, K. Morita, Y. Fukumoto, and H. Nakano, Prog. Theor. Exp. Phys.
2022, 113I01, (2022).

GOHDA, Yoshihiro [ C class; 6000 (B), 450 (C) ] (74)
— Control of magnetic anisotropy by strain and electric polarization at interfaces
1. Giant converse magnetoelectric eﬀect in a multiferroic heterostructure with polycrystalline Co 2 FeSi
S. Fujii, T. Usami, Y. Shiratsuchi, A.M. Kerrigan, A.M. Yatmeidhy, S. Yamada, T. Kanashima,

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R. Nakatani, V.K. Lazarov, T. Oguchi, Y. Gohda, and K. Hamaya, NPG Asia Mater. 14, 43(2022).
DOI:10.1038/s41427-022-00389-1
2.

√√
Superconductivity in a two monolayer thick indium ﬁlm on Si(111) 3 × 3-B
T. Ogino, I. Seo, H. Tajiri, M. Nakatake, S. Takakura, Y. Sato, Y. Hasegawa, Y. Gohda, K.
Nakatsuji, and H. Hirayama, Phys. Rev. B 106, 045423 (2022).
DOI:10.1103/PhysRevB.106.045423

3.

Origin of anisotropic magnetoresistance tunable with electric ﬁeld in Co 2 FeSi/BaTiO3 multiferroic
inetrfaces
S. Tsuna, R. Costa-Amaral, and Y. Gohda, J. Appl. Phys. 132, 234101 (2022).
DOI:10.1063/5.0128149

4.

First-principles phonon calculations of neodymium-magnet compounds
S. Tsuna and Y. Gohda, J. Appl. Phys. 133, 115103 (2023).
DOI:10.1063/5.0142945

5.

Structures of Sm-Cu intermetallics with Fe as subphase candidates in SmFe 12 -based permanent
magnets studied by ﬁrst-principles thermodynamics
S. Nishino and Y. Gohda, Jpn. J. Appl. Phys. 62, 030902 (2023).
DOI:10.35848/1347-4065/acc0b8

GOHLKE, Matthias [ C class; 3000 (B), 150 (C) ] (263)— Ground state and dynamical properties of the J1 J2 K-Heisenberg model on the square lattice
GONOME, Hiroki [ C class; 3800 (B), 550 (C) ] (104)— Study of the principle of photothermal conversion by ab initio calculations
HAGITA, Katsumi [ C class; 1000 (B), 150 (C) ] (286)— Eﬀect of chain crossing prohibition on phase-separated structure of block copolymers
1. Ring-Filling Eﬀect on StressStrain Curves of Randomly End-Linked Tetra-Arm Prepolymers
K. Hagita, T. Murashima, T. Ohkuma, H. Jinnai, Macromolecules 55, 6547 (2022).
DOI:10.1021/acs.macromol.2c00451
2.

Topological transition in multicyclic chains with structural symmetry inducing stress-overshootphenomena in multicyclic/linear blends under biaxial elongational ﬂow
T. Murashima, K. Hagita, T. Kawakatsu, Macromolecules 55, 9358 (2022).
DOI:10.1021/acs.macromol.2c01579

3.

Practical compatibility between self-consistent ﬁeld theory and dissipative particle dynamics
K. Hagita, T. Murashima, Polymer 269, 125733 (2023).
DOI:10.1016/j.polymer.2023.125733

4.

Lamellar Domain Spacing of Symmetric Linear, Ring, and Four-Arm-Star Block Copolymer
BlendsK. Hagita, T. Murashima, and T. Kawakatsu Macromolecules 55, 8021 (2022).
DOI:10.1021/acs.macromol.2c00500

5.

分子シミュレーションを用いたフェノール樹脂の構造-物性相関解析
首藤靖幸, 和泉篤士, 萩田克美, 柴山充弘ネットワークポリマー 43, 246 (2022).
DOI:10.11364/networkedpolymer.43.6 246

6.

Molecular Dynamics of Topological Barriers on the Crystallization Behavior of Ring Polyethylene

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Melts with Trefoil Knots
K. Hagita, T. Murashima, N. Sakata, K. Shimokawa, T. Deguchi, E. Uehara, and S. Fujiwara
Macromolecules 56, 15 (2023).
DOI:10.1021/acs.macromol.2c01843

HAMADA, Ikutaro [ C class; 8200 (B), 0 (C) ] (67)
— Density functional theory study of adsorption and reaction of molecules on metal surfaces— Density functional theory study of molecule/metal interfaces
1. Interaction of water with nitrogen-doped graphene
A. F. Z. Abidin and I. Hamada, Phys. Rev. B 105, 075416 (2022).
DOI:10.1103/PhysRevB.105.075416
2.

Comparative density functional theory study for predicting oxygen reduction activity of singleatom catalyst
A. F. Z. Abidin and I. Hamada, Surf. Sci. 724, 122144 (2022).
DOI:10.1016/j.susc.2022.122144

3.

Phonon dispersion of the organic semiconductor rubrene
K. Takada, K. Yoshimi, S. Tsutsui, K. Kimura, K. Hayashi, I. Hamada, S. Yanagisawa, N. Kasuya,
S. Watanabe, J. Takeya, and Y. Wakabayashi, Phys. Rev. B 105, 205205 (2022).
DOI:10.1103/PhysRevB.105.205205

Data Repository
Interaction of water with nitrogen-doped graphene
DOI:10.24435/materialscloud:6b-bc
Comparative density functional theory study for predicting oxygen reduction activity of single-atom
catalyst
DOI:10.24435/materialscloud:hv-yd

HAMAGUCHI, Satoshi [ C class; 8600 (B), 800 (C) ] ()— Study on plasma resistance of metal oxides
HAMAMOTO, Yuji [ C,D class; 4400 (B), 0 (C) ] (99, 100)
— First principles study of the adsorption structure of helicene on the Ag(111) surface— Global search for metastable structures of silicene on the Ag(111) surface by Gaussian process regression
1. Hybridization-Induced Image Potential States with Large Eﬀective Mass in Lead Phthalocyanine
Overlayers on Graphene
Y. Hamamoto, H. Sawada, S. A. Wella, K. Inagaki, I. Hamada, and Y. Morikawa, J. Phys. Chem.
C 126, 10855 (2022).
DOI:10.1021/acs.jpcc.2c01652

HARADA, KENJI [ C class; 1200 (B), 600 (C) ] (275)— Tensor data analysis by tensor network
HARASHIMA, Yosuke [ C class; 1000 (B), 0 (C) ] (150)— Exponent puzzle for metal-insulator transition in doped semiconductors
1. Systematic search for stabilizing dopants in ZrO2 and HfO2 using ﬁrst-principles calculations
Y. Harashima, H. Koga, Z. Ni, T. Yonehara, M. Katouda, A. Notake, H. Matsui, T. Moriya, M.
K. Si, R. Hasunuma, A. Uedono, and Y. Shigeta, IEEE Trans. Semicon. Man., accepted.
DOI:10.1109/TSM.2023.3265658

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

HARUYAMA, Jun [ C class; 3200 (B), 400 (C) ] (110)— Electrochemical reaction analysis using density functional calculation + implicit solvation model 4
1. First-Principles Study for Water Adsorption Layers on Platinum Surface
J. Haruyama, T. Sugimoto, and O. Sugino Vacuum and Surface Science 65, 355 (2022).
DOI:10.1380/vss.65.1
2.

Eﬀect of Nitrogen Doping and Oxygen Vacancy on the Oxygen Reduction Reaction on the Tetragonal Zirconia(101) Surface
S. Muhammady, J. Haruyama, S. Kasamatsu, and O. Sugino, Vacuum and Surface Science 126,
15662 (2022).
DOI:10.1021/acs.jpcc.2c04132

3.

First-principles study of water adsorption monolayer on Pt(111): adsorption energy and secondorder nonlinear susceptibility
J. Haruyama, T. Sugimoto, and O. Sugino, submitted to Phys. Rev. Mater.

Data Repository
QE input for H20 adsorption monolayer system on Pt(111)https://isspns-gitlab.issp.u-tokyo.ac.jp/j-haruyama/Pt111 H2O-monolayer

HASHIMOTO, Tamotsu [ C class; 1200 (B), 200 (C) ] (276)— Molecular dynamics simulation of BaTiO3 nano structure II
1. Domain structures in dielectric polarization vortex of BaTiO3 nanoclusters: A shell model molecular dynamics study
T. Hashimoto and H. Moriwake, Physica B, 656, 414768 (2023).
DOI:10.1016/j.physb.2023.414768
2.

Domain structures of rhombohedral BaTiO3 by a shell model
T. Hashimoto and H. Moriwake, submitted

HATSUGAI, Yasuhiro [ C class; 4400 (B), 350 (C) ] (249)— Numerical studies for science of bulk-edge correspondence and topological phases
1. Machine Learning Study on the Flat-Band States Constructed by Molecular-Orbital Representation with Randomness
T. Kuroda, T. Mizoguchi, H. Araki, and Y. Hatsugai J. Phys. Soc. Jpn. 91, 044703 (2022).
DOI:10.7566/JPSJ.91.044703
2.

Adiabatic continuity of the spinful quantum Hall states
K. Kudo and Y. Hatsugai Phys. Rev. B 106, 075120 (2022).
DOI:10.1103/PhysRevB.106.075120

3.

Observation of bulk-edge correspondence in topological pumping based on a tunable electric circuit
K. Yatsugi, T. Yoshida, T. Mizoguchi, Y. Kuno, H. Iizuka, Y. Tadokoro, and Y. Hatsugai Commun.
Phys. 5, 180 (2022).
DOI:10.1038/s42005-022-00957-5

4.

Higher-Order Topological Insulator on a Martini Lattice and Its Square Root Descendant
D. Matsumoto, T. Mizoguchi, and Y. Hatsugai J. Phys. Soc. Jpn. 92, 034705 (2023).
DOI:10.7566/JPSJ.92.034705

5.

Molecular-orbital representation with random U(1) variables

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

T. Mizoguchi and Y. Hatsugai Phys. Rev. B 107, 094201 (2023).
DOI:10.1103/PhysRevB.107.094201
6.

Reduction of one-dimensional non-Hermitian point-gap topology by interactions
T. Yoshida and Y. Hatsugai Phys. Rev. B 106, 205147 (2022).
DOI:10.1103/PhysRevB.106.205147

7.

Discriminant indicator with generalized rotational symmetry
H. Wakao, T. Yoshida, and Y. Hatsugai Phys. Rev. B 105, 214103 (2022).
DOI:10.1103/PhysRevB.105.214103

8.

Unconventional gapless semiconductor in an extended martini lattice in covalent honeycomb materials
T. Mizoguchi, Y. Gao, M. Maruyama, Y. Hatsugai, and S. Okada Phys. Rev. B 107, L121301(2023).
DOI:10.1103/PhysRevB.107.L121301

9.

Fate of exceptional points under interactions: Reduction of topological classiﬁcations
T. Yoshida and Y. Hatsugai Phys. Rev. B 107, 075118 (2023).
DOI:10.1103/PhysRevB.107.075118

10. A symmetry-protected exceptional ring in a photonic crystal with negative index media
T. Isobe, T. Yoshida, and Y. Hatsugai arXiv:2212.11090
11. Topological pump of SU(Q) quantum chain and Diophantine equation
Y. Hatsugai and Y. Kuno, arXiv:2210.11646

HAYAMI, Satoru [ C class; 5600 (B), 450 (C) ] (236)— Numerical simulations combined with machine learning to search for magnetic skyrmion in frustrated
magnets
1. Spin Conductivity Based on Magnetic Toroidal Quadrupole Hidden in Antiferromagnets
S. Hayamia and M. Yatsushiro, J. Phys. Soc. Jpn. 91, 063702 (2022).
DOI:10.1103/PhysRevB.105.155157
2.

Multifarious skyrmion phases in centrosymmetric trilayer magnets
S. Hayami, Phys. Rev. B 105, 184426 (2022).
DOI:10.1103/PhysRevB.105.184426

3.

Rectangular and square skyrmion crystals on a centrosymmetric square lattice with easy-axis
anisotropy
S. Hayami, Phys. Rev. B 105, 174437 (2022).
DOI:10.1103/PhysRevB.105.174437

4.

Antisymmetric thermopolarization by electric toroidicity
J. Nasu and S. Hayami, Phys. Rev. B 105, 245125 (2022).
DOI:10.1103/PhysRevB.105.245125

5.

Skyrmion crystal with integer and fractional skyrmion numbers in a nonsymmorphic lattice structure with the screw axis
S. Hayami, Phys. Rev. B 105, 224411 (2022).
DOI:10.1103/PhysRevB.105.224411

6.

Skyrmion crystal under D3h point group: Role of out-of-plane Dzyaloshinskii-Moriya interaction
S. Hayami and R. Yambe, Phys. Rev. B 105, 224423 (2022).

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

DOI:10.1103/PhysRevB.105.224423
7.

Square skyrmion crystal in centrosymmetric systems with locally inversion-asymmetric layers
S. Hayami, J. Phys.: Condens. Matter 34, 365802 (2022).
DOI:10.1088/1361-648X/ac7bcb

8.

Nonlinear spin Hall eﬀect in PT-symmetric collinear mag- nets
S. Hayami, M. Yatsushiro, and H. Kusunose, Phys. Rev. B 106, 024405 (2022).
DOI:10.1103/PhysRevB.106.024405

9.

Nonlinear nonreciprocal transport in antiferromagnets free from spin-orbit coupling
S. Hayami and M. Yatsushiro, Phys. Rev. B 106, 014420 (2022).
DOI:10.1103/PhysRevB.106.014420

10. Stability of Skyrmion Crystal Phase in Centrosymmetric Distorted Triangular-Lattice Antiferromagnets
S. Hayami, J. Phys. Soc. Jpn. 91, 093701 (2022).
DOI:10.7566/JPSJ.91.093701
11. Magnetic Hedgehog Lattice in a Centrosymmetric Cubic Metal
S. Okumura, S. Hayami, Y. Kato, and Y. Motome, J. Phys. Soc. Jpn. 91, 093702 (2022).
DOI:10.7566/JPSJ.91.093702
12. Nonreciprocal Transport in Noncoplanar Magnetic Systems without Spin-Orbit Coupling, Net
Scalar Chirality, or Magnetization
S. Hayami and M. Yatsushiro, J. Phys. Soc. Jpn. 91, 094704 (2022).
DOI:10.7566/JPSJ.91.094704
13. Engineering a skyrmion crystal in ferromagnetic/antiferromagnetic bilayers based on magnetic
frustration mechanism
K. Okigami, R. Yambe, and, S. Hayami, J. Phys. Soc. Jpn. 91, 103701 (2022).
DOI:10.7566/JPSJ.91.103701
14. Ferroaxial moment induced by vortex spin texture
S. Hayami, Phys. Rev. B 106, 144402 (2022).
DOI:10.1103/PhysRevB.106.144402
15. Electric Ferro-Axial Moment as Nanometric Rotator and Source of Longitudinal Spin Current
S. Hayami, R. Oiwa, and H. Kusunose, J. Phys. Soc. Jpn. 91, 113702 (2022).
DOI:10.7566/JPSJ.91.113702
16. Skyrmion and vortex crystals in the Hubbard model
K. Kobayashi and S. Hayami, Phys. Rev. B 106, L140406 (2022).
DOI:10.1103/PhysRevB.106.L140406
17. Zero-ﬁeld skyrmion, meron, and vortex crystals in centrosymmetric hexagonal magnets
S. Hayami, J. Magn. Magn. Mater. 564, 170036 (2022).
DOI:10.1016/j.jmmm.2022.170036
18. Magnetic Toroidal Moment under Partial Magnetic Order in Hexagonal Zigzag-Chain Compound
Ce3 TiBi5
S. Hayami and H. Kusunose, J. Phys. Soc. Jpn. 91, 123701 (2022).
DOI:10.7566/JPSJ.91.123701
19. Eﬀective spin model in momentum space: Toward a systematic understanding of multiple-Q390

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

instability by momentum-resolved anisotropic exchange interactions
R. Yambe and S. Hayami, Phys. Rev. B 106, 174437 (2022).
DOI:10.1103/PhysRevB.106.174437
20. Orthorhombic distortion and rectangular skyrmion crystal in a centrosymmetric tetragonal host
S. Hayami, J. Phys.: Mater. 6, 014006 (2023).
DOI:10.1088/2515-7639/acab89
21. Stacking-dependent nonreciprocal transport in magnetic skyrmions
S. Hayami, J. Magn. Magn. Mater. 568, 170420 (2023).
DOI:10.1016/j.jmmm.2023.170420
22. Ferrochiral, antiferrochiral, and ferrichiral skyrmion crystals in an itinerant honeycomb magnet
R. Yambe and S. Hayami, Phys. Rev. B 107, 014417 (2023).
DOI:PhysRevB.107.014417
23. Multiple-Q instability under fourth-order inplane single-ion anisotropy
S. Hayami, J. Magn. Magn. Mater. 570, 170507 (2023).
DOI:10.1016/j.jmmm.2023.170507
24. Widely-sweeping magnetic ﬁeldtemperature phase diagrams for skyrmion-hosting centrosymmetric tetragonal magnets
S. Hayami and Y. Kato, J. Magn. Magn. Mater. 571, 170547 (2023).
DOI:10.1016/j.jmmm.2023.170547
25. Cluster Toroidal Multipoles formed by Electric-Quadrupole and Magnetic-Octupole Trimers: A
Possible Scenario for Hidden Orders in Ca5 Ir3 O12
S. Hayami and H. Kusunose, J. Phys. Soc. Jpn. 92, 033702 (2023).
DOI:10.7566/JPSJ.92.033702

HAYASAKA, Hiroshi [ B class; 400 (B), 70 (C) ] (330)— Atomistic model study on the coercivity mechanism of permanent magnets
1. Microscopic study on the angular dependence of coercivity at zero and ﬁnite temperatures
H. Hayasaka, M. Nishino, and S. Miyashita Phys. Rev. B 105, 224414 (2022).
DOI:10.1103/PhysRevB.105.224414
2.

Microscopic origin of coercivity enhancement by dysprosium substitution into neodymium permanent magnets
M. Nishino, H. Hayasaka, and S. Miyashita Phys. Rev. B 106, 054422 (2022).
DOI:10.1103/PhysRevB.106.054422

HIDA, Kazuo [ B class; 300 (B), 60 (C) ] (341)— Numerical Study of One Dimensional Frustrated Quantum Spin Systems
HIGUCHI, Yuji [ C class; 10200 (B), 900 (C) ] (219)— Correlation between the structure of charged phospholipid bilayer membrane and ion distribution bymulti-scale simulation— Structure and dynamics of charged phospholipid bilayer membrane and water molecules
HINUMA, Yoyo [ B class; 400 (B), 0 (C) ] (183)— Evaluating surface oxygen vacancy stability in complicated oxides for catalyst informatics
1. Trends in Surface Oxygen Formation Energy in Perovskite Oxides
Y. Hinuma, S. Mine, T. Toyao and K. Shimizu, ACS Omega 2022, 7, 18427

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

DOI:10.1021/acsomega.2c00702

HIRATSUKA, Masaki [ B class; 500 (B), 80 (C) ] ()— Validation of a Machine Learning Method for Predicting Vibration Spectra
HIYAMA, Miyabi [ B class; 400 (B), 80 (C) ] (327)— Theoretical study for ﬁreﬂy bioluminescence substrate analogs and related molecules
HOSHI, Takeo [ C class; 3600 (B), 350 (C) ] (255)— HPC-based fusion of experiment analysis simulation and data-driven science
1. Two-stage data-analysis method for total-reﬂection high-energy positron diﬀraction (TRHEPD)
K. Tanaka, I. Mochizuki, T. Hanada, A. Ichimiya, T. Hyodo, and T. Hoshi, JJAP Conf. Series
9, 011301 (2023).
DOI:10.56646/jjapcp.9.0 011301
2.

Data-analysis software framework 2DMAT and its application to experimental measurements for
two-dimensional material structures
Y. Motoyama, K. Yoshimi, I. Mochizuki, H. Iwamoto, H. Ichinose, and T. Hoshi, Comp. Phys.
Commun. 280, 108465 (2022).
DOI:10.1016/j.cpc.2022.108465

3.

sim-trhepd-rheed Open-source simulator of total-reﬂection high-energy positron diﬀraction (TRHEPD)
and reﬂection high-energy electron diﬀraction (RHEED)
T. Hanada, Y. Motoyama, K. Yoshimi, and T. Hoshi, Comp. Phys. Commun. 277, 108371(2022).
DOI:10.1016/j.cpc.2022.108465

4.

Data-driven Sensitivity Analysis in Surface Structure Determination by Total-Reﬂection HighEnergy Positron Diﬀraction (TRHEPD)
T. Hoshi, I. Mochizuki, S. Tanaka, T. Hyodo, and K. Hukushima Photon Factory Highlights 2021,
46 (2022).

Data Repository
2DMAT Gallery
https://isspns-gitlab.issp.u-tokyo.ac.jp/2dmat-dev/2dmat-gallery

HOTTA, Takashi [ C class; 4200 (B), 0 (C) ] (199)— Research of Three-Channel Kondo Eﬀect Emerging from Holmium Ions
1. Eﬀect of Local Jahn-Teller Phonons on Quantum Critical Point in a Two-Orbital Anderson Model
Y. Matsumoto and T. Hotta, J. Phys. Soc. Jpn. 91, 124712 (2022).
DOI:10.7566/JPSJ.91.124712

HUKUSHIMA, Koji [ C class; 5800 (B), 300 (C) ] (234, 235)
— Molecular dynamics study of phase separation induced by binding molecules— Tensor renormalization-group study of random spin systems
1. Estimating Distributions of Parameters in Nonlinear State Space Models with Replica Exchange
Particle Marginal MetropolisHastings Method
H. Inoue, K. Hukushima, and T. Omori Entropy 24, 115 (2022).
2.

Phase transition in compressed sensing with horseshoe prior
Y. Nagano and K. Hukushima Phys. Rev. E 107, 034126 (2023).

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

3.

Statistical-mechanical Study of Deep Boltzmann Machine Given Weight Parameters after Training
by Singular Value Decomposition
Y. Ichikawa and K. Hukushima J. Phys. Soc. Jpn. 91, 114001 (2022).

4.

Absence of Phase Transition in Random Language Model
K. Nakaishi and K. Hukushima Phys. Rev. Res. 4, 023156 (2022).

IDO, Kota [ C class; 4600 (B), 450 (C) ] (242)— Stability of quantum spin liquids in frustrated quantum spin systems
IITAKA, Toshiaki [ C class; 1600 (B), 250 (C) ] (273)— Finite temperature calculation of quantum manybody system using random phase product state andneural network wavefunction
IKEDA, Hiroaki [ B class; 400 (B), 80 (C) ] ()— ﬁrst principles calculations in quantum liquid crystals
IKUHARA, Yuichi [ C class; 5800 (B), 0 (C) ] (82)
— Exploring singular atomic arrangements via ﬁrst-principles calculations— Molecular dynamics simulation of grain boundaries in perovskite oxide
IMADA, Masatoshi [ E class; 32000 (B), 2550 (C) ] (23)— Integrated spectroscopic studies by combining experimental data and high precision large-scale computation for strongly correlated electrons— Systematic ab initio studies on high temperature superconductivity of transition metal oxides
1. Unconventional exciton evolution from the pseudogap to superconducting phases in cuprates
A. Singh, H. Y. Huang, J. D. Xie, J. Okamoto, C. T. Chen, T. Watanabe, A. Fujimori, M. Imada,and D. J. Huang, Nat. Commun. 13, 7906 (2022).
DOI:10.1038/s41467-022-35210-82.

Ab initiolow-energy eﬀective Hamiltonians for the high-temperature superconducting cuprates
Bi2 Sr2 CuO6 , Bi2 Sr2 CaCu2 O8 , HgBa2 CuO4 , and CaCuO2
Jean-Baptiste Morée, M. Hirayama, M. T. Schmid, Y. Yamaji, and M. Imada, Phys. Rev. B
106, 235150 (2022).
DOI:10.1103/PhysRevB.106.235150

3.

Unconventional dual 1D-2D quantum spin liquid revealed by ab initio studies on organic solids
familyK. Ido, K. Yoshimi, T. Misawa, and M. Imada, npj Quantum Mater. 7, 48 (2022).
DOI:0.1038/s41535-022-00452-8

4.

Optimized implementation for calculation and fast-update of Pfaﬃans installed to the open-source
fermionic variational solver mVMC
R.-Q. G. Xu, T. Okubo, S. Todo, and M. Imada, Compt. Phys. Commun., 277, 108375 (2022).
DOI:10.1016/j.cpc.2022.108375

INAGAKI, Kouji [ B class; 400 (B), 80 (C) ] (175)— First-principles calculation of graphitization of diamond surface and its exfoliation process
INAOKA, Takeshi [ B class; 300 (B), 60 (C) ] (185)— Search and realization of novel electronic properties of surfaces and interfaces and of nanostructures
1. Formation of an iodine metallic band in the x-form phthalocyanine crystal
T. Inaoka, to be submitted.

393

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of TokyoISHIBASHI, Shoji [ C class; 2400 (B), 0 (C) ] (121)— Prediction of properties of organic ferroelectrics and piezoelectrics by ﬁrst-principles calculation
1. Competition of Polar and Antipolar States Hidden Behind a Variety of Polarization Switching
Modes in Hydrogen-Bonded Molecular Chains
S. Horiuchi, H. Minemawari, and S. Ishibashi, Mater. Horizons, Advance Article (2023).
DOI:10.1039/d2mh01530g

ISHIDA, Kunio [ C class; 2400 (B), 400 (C) ] ()— Initial dynamics of photoinduced cooperativity by quantized light
ISHII, Fumiyuki [ C class; 9800 (B), 450 (C) ] (60)
— Development of an accurate and eﬃcient method for calculating the anomalous Hall eﬀect— First-principles calculation of thermoelectric properties in atomic-layer and topological materials
1. First-principles LCPAO Approach to Insulators Under Finite Electric Fields with Forces
N. Yamaguchi and F. Ishii, Comput. Phys. Commun. 280 (2022) 108487.
2.

First-principles calculation of anomalous Hall and Nernst conductivity by local Berry phase
H. Sawahata, N. Yamaguchi, S. Minami, and F. Ishii, Phys. Rev. B, 107, (2023) 024404.

3.

First-principles study of anomalous Hall eﬀect and anomalous Nernst eﬀect in Fe 2 Si
T. Tominaga N. Yamaguchi, H. Sawahata, and F. Ishii, Jpn. J. Appl. Phys. 62, (2023) SD1019.

4.

Persistent spin helix on a diamond surface
H. P. Kadarisman, N. Yamaguchi, and F. Ishii, Appl. Phys. Express, 16 (2023) 023001.

ISOBE, Masaharu [ B class; 500 (B), 0 (C) ] (324)— Equilibration and glass transition in the dense hard sphere systems
1. Anomalous phase transition in self-propelled hard disk systems
N. Murase and M. Isobe, Proceedings of the 28th Symposium on Traﬃc Flow and Self-driven
Particles, 28, 9 (2021).

JESCHKE, Harald [ C class; 4800 (B), 0 (C) ] (247, 248)
— Fluctuation exchange approximation calculations for the superconducting transition temperatures andpairing symmetries of organic charge transfer salts
JOUTSUKA, Tatsuya [ C class; 1200 (B), 0 (C) ] (134)— Analyzing Reaction Mechanism of Charge Transfer in Catalysis by Electronic Structure Calcutions
1. Understanding the structure of Cu-doped MgAl2 O4 for CO2 hydrogenation catalyst precursor
using experimental and computational approaches
T. Joutsuka,* R. Hamamura, K. Fujiwara, T. Honma, M. Nishijima, and S. Tada, Int. J. Hydrog.
Energy 47, 21369 (2022).
DOI:10.1016/j.ijhydene.2022.04.295
2.

Active sites on Znx Zr1 − x O2 − x solid solution catalysts for CO2 -to-methanol hydrogenation
S. Tada, N. Ochiai, H. Kinoshita, M. Yoshida, N. Shimada, T. Joutsuka, M. Nishijima, T. Honma,
N. Yamauchi, Y. Kobayashi, and K. Iyoki, ACS Catal. 12, 7748 (2022).
DOI:10.1021/acscatal.2c01996

3.

Molecular Mechanism of Autodissociation in Liquid Water: Ab Initio Molecular Dynamics Simulations

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T. Joutsuka, J. Phys. Chem. B 126, 24, 4565 (2022).
DOI:10.1021/acscatal.2c01996
4.

Catalysis of Surface Dispersed Cu2+ Species on t-ZrO2 : Square-Planar Cu Catalyzed CrossCoupling of Arylboronic Acid and Imidazole
M. Kondo, T. Joutsuka, K. Fujiwara, T. Honma, and S. Tada, Catal. Sci. Technol. 13, 2247
(2023).
DOI:10.1039/D3CY00024A

5.

Study of Cu-growth feature by selective low-pressure chemical vapor deposition using a CuI precursor
G. Toyoda, H. Kikuchi, S. Yamauchi, T. Joutsuka, T. Fuse, and Y. Kubota, Jpn. J. Appl. Phys.
62, SH1002 (2023).
DOI:10.35848/1347-4065/acc257

6.

Adsorption of CO2 on Amorphous and Crystalline Zirconia: A DFT and Experimental Study
T. Joutsuka and S. Tada, J. Phys. Chem. C 127, 6998 (2023).
DOI:10.1021/acs.jpcc.3c01185

KADOWAKI, Hiroaki [ B class; 400 (B), 70 (C) ] (329)— quantum pyrochrole magnet
KAGESHIMA, Hiroyuki [ C class; 1000 (B), 0 (C) ] (148)— Study on structural elementary excitations at solid surfaces and interfaces
1. First-principles study on silicon emission from interface into oxide during silicon thermal oxidation
H. Kageshima, T. Akiyama, and K. Shiraishi, Materials Science in Semiconductor Processing 162,
107527 (2023).
DOI:10.1016/j.mssp.2023.107527

KANEKO, Ryui [ B class; 1200 (B), 180 (C) ] (277, 279)
— Numerical study of quantum many-body scars in bosonic systems— Tensor-network study of the quench dynamics of the Kitaev model
1. Ground-state phase diagram of a spin-1/2 frustrated XXZ ladder
T. Ogino, R. Kaneko, S. Morita, and S. Furukawa, Phys. Rev. B 106, 155106 (2022).
DOI:10.1103/PhysRevB.106.155106
2.

Rényi entanglement entropy after a quantum quench starting from insulating states in a free boson
system
D. Kagamihara, R. Kaneko, S. Yamashika, K. Sugiyama, R. Yoshii, S. Tsuchiya, and I. Danshita,
Phys. Rev. A 107, 033305 (2023).
DOI:10.1103/PhysRevA.107.033305

3.

Dynamics of correlation spreading in low-dimensional transverse-ﬁeld Ising models
R. Kaneko, I. Danshita, arXiv:2301.01407.
DOI:10.48550/arXiv.2301.01407

KARIYADO, Toshikaze [ B class; 300 (B), 50 (C) ] (343)— Novel phenomena in mismatched multilayer systems: Search for new candidate materials and analysis
of electronic structures
1. Twisted bilayer BC3 : Valley interlocked anisotropic ﬂat bands
T. Kariyado, Phys. Rev. B 107, 085127 (2023).
DOI:10.1103/PhysRevB.107.085127

395

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of TokyoKASAMATSU, Shusuke [ E class; 14000 (B), 1100 (C) ] (51, 374)
— Understanding structure-property relationships of disordered materials using ﬁrst-principles based
high-throughput simulation framework
1. Facilitating ab initio conﬁgurational sampling of multicomponent solids using an on-lattice neuralnetwork model and active learning
S. Kasamatsu, Y. Motoyama, K. Yoshimi, U. Matsumoto, A. Kuwabara, and T. Ogawa, J. Chem.
Phys. 157, 104114 (2022).
DOI:10.1063/5.0096645
2.

Structural Analysis of Amorphous GeO2 under High Pressure Using Reverse Monte Carlo Simulations
K. Matsutani, A. Yamauchi, S. Kasamatsu, and T. Usuki J. Phys. Soc. Jpn. 91, 124601 (2022).
DOI:10.7566/JPSJ.91.124601

3.

Probing Local Environments of Oxygen Vacancies Responsible for Hydration in Sc-Doped Barium
Zirconates at Elevated Temperatures: In SituXray Absorption Spectroscopy, Thermogravimetry,and Active Learning Ab Initio Replica Exchange Monte Carlo Simulations
K. Hoshino, S. Kasamatsu, J. Hyodo, K. Yamamoto, H. Setoyama, T. Okajima, and Y. Yamazaki,
Chem. Mater. 35, 2289 (2023).
DOI:10.1021/acs.chemmater.2c02116

KATO, Takeo [ B class; 500 (B), 80 (C) ] (321)— Numerical Study of Spin Transport in Magnetic Junctions
1. Spin Hall magnetoresistance in quasi-two-dimensional antiferromagnetic-insulator/metal bilayer
systems
T. Ishikawa, M. Matsuo, and T. Kato, Phys. Rev. B 107, 054426 (2023).
DOI:10.1103/PhysRevB.107.054426

KATOW, Hiroki [ C class; 1000 (B), 0 (C) ] (146)— Development of First Principles methods for Light-Matter Interaction
1. First-Principles Study of the Optical Dipole Trap for Two-Dimensional Excitons in Graphane
H. Katow, R. Akashi, Y. Miyamoto, and S. Tsuneyuki, Phys. Rev. Lett. 129, 047401 (2022).
DOI:10.1103/PhysRevLett.129.047401

KAWAKATSU, Toshihiro [ C class; 6200 (B), 0 (C) ] (232)— Multiscale Flow Simulations on Complex Fluids Undergoing Phase Transition
KAWAMURA, Hikaru [ C class; 2400 (B), 0 (C) ] (272)— Novel order in frustrated magnets— Novelorderinfrustratedmagnets
1. Skyrmion crystal in the RKKY system on the two-dimensional triangular lattice
K. Mitsumoto and H. Kawamura, Phys. Rev. B 105, 094427(2022).
DOI:10.1103 /PhysRevB.105.094427
2.

Emergent skyrmion-based chiral order in zero-ﬁeld Heisenberg antiferromagnets on the breathing
kagome lattice
K. Aoyama and H. Kawamura, Phys. Rev. B 105, L100407 (2022).
DOI:10.1103/PhysRevB.105.L100407

3.

Spin Dynamics Simulation of the Z2 -vortex Fluctuations

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Y.P. Mizuta, K. Aoyama, K. Tomiyasu, M. Matsuura, and H. Kawamura, J. Phys. Soc. Jpn.
91, 035001 (2022).
DOI:10.7566/JPSJ.91.035001
4.

Hedgehog lattice and ﬁeld-induced chirality in breathing-pyrochlore Heisenberg antiferromagnets
K. Aoyama and H. Kawamura, Phys. Rev. B 106, 064412 (2022).
DOI:10.1103/PhysRevB.106.064412

5.

Zero-ﬁeld miniature skyrmion crystal and chiral domain state in breathing-kagome antiferromagnet
K. Aoyama and H. Kawamura, J. Phys. Soc. Jpn. 92, 033701 (2023).
DOI:10.7566/JPSJ.92.033701

KAWANO, Shoya [ B,D class; 1150 (B), 0 (C) ] (136, 138)
— First-principles calculation of oxygen defects on the surface of titanium dioxide TiO2
— Thermal conductivity calculation for high thermal conductive insulator
KAWASHIMA, Naoki [ E class; 35500 (B), 2750 (C) ] (214)— Loop-gas representation of classical and quantum statistical-mechanical models and its tensor-network
calculation
1. Non-monotonic behavior of the Binder parameter in discrete spin systems
H. Watanabe, Y. Motoyama, S. Morita, and N. Kawashima, Prog. Theor. Exp. Phys. 033A02(2023).
DOI:10.1093/ptep/ptad022
2.

TeNeS: Tensor Network Solver for Quantum Lattice Systems
Y. Motoyama, T. Okubo, K. Yoshimi, S. Morita, T. Kato, and N. Kawashima, Comput. Phys.
Commun. 279, 108437 (2022).
DOI:10.1016/j.cpc.2022.108437

3.

Reply to Comments on ”Universal and Non-Universal Correction Terms of Bose Gases in Dilute
Region: A Quantum Monte Carlo Study”
A. Masaki-Kato, Y. Motoyama, and N. Kawashima, J. Phys. Soc. Jpn. 91, 066002 (2022).

KITAO, Akio [ C class; 4600 (B), 400 (C) ] (243)— Eﬃcient sampling simulation of the soft modes signiﬁcantly contribute to protein properties
1. Inhibition of the hexamerization of SARS‐CoV‐2 endoribonuclease and modeling of RNA structures bound to the hexamer
D. P. Tran, Y. Taira, T. Ogawa, R. Misu, Y. Miyazawa, A. Kitao, Sci. Rep. 12 (2022) 3860.
DOI:10.1038/s41598-022-07792-2
2.

Structure of MotA, a ﬂagellar stator protein, from hyperthermophile
T. Nishikino, N. Takekawa, D. P. Tran, J. Kishikawa, M. Hirose, S. Onoe, S. Kojima, M. Homma,
A. Kitao, T. Kato, and K. Imada, Biochem. Biophys. Res. Commun. 631 (2022) 78.
DOI:10.1016/j.bbrc.2022.09.072

KOBAYASHI, Akito [ B class; 400 (B), 80 (C) ] (209, 376)
— Systematic elucidation of ordered states in organic Dirac electron systems
1. Gap opening mechanism for correlated Dirac electrons in organic compounds α-(BEDT-TTF)2I3
and α-(BEDT-TSeF)2I3
D. Ohki, K. Yoshimi, A. Kobayashi, and T. Misawa Phys. Rev. B 107, L041108 (2022).
DOI:10.1103/PhysRevB.107.L041108

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

2.

Fragment-orbital-dependent spin ﬂuctuations in the single-component molecular conductor [Ni(dmdt)2]
T. Kawamura and A. Kobayashi Phys. Rev. B 105, 205145 (2022).
DOI:10.1103/PhysRevB.105.205145

3.

Interaction-induced quantum spin Hall insulator in the organic Dirac electron system alpha(BEDT-TSeF)2I3
D. Ohki, K. Yoshimi, and A. Kobayashi Phys. Rev. B 105, 205123 (2022).
DOI:10.1103/PhysRevB.105.205123

Data Repository
H-wave Gallery
https://isspns-gitlab.issp.u-tokyo.ac.jp/hwave-dev/hwave-gallery

KOBAYASHI, Katsuyoshi [ B class; 600 (B), 90 (C) ] (159)— Theoretical study on electronic properties of new nanoscale surfaces and interfaces
1. Formation of monolayer V5 Se8 from multilayer VSe2 ﬁlms via V- and Se-desorption
K. Sumida, S. Kusaka, Y. Takeda, K. Kobayashi, and T. Hirahara Phys. Rev. B 106, 195421(2022).
DOI:10.1103/PhysRevB.106.195421

KOBAYASHI, Nobuhiko [ C class; 4800 (B), 400 (C) ] (86)
— Quantum transport theory by large scale ﬁrst-principles electron transport calculations
1. Electronic and magnetic properties of CoSb3 , Cr-doped CoSb3 , and related compound thin ﬁlms
K. Kobayashi, H. Takaki, M. Shimono, H. Ishii, N. Kobayashi, K. Hirose, and T. Mori Jpn. J.
Appl. Phys. 62 SC1046 (2023).
2.

Single-molecule detection with enhanced Raman scattering of tungsten oxide nanostructure
Y. Shingaya, H. Takaki, N. Kobayashi, M. Aono; T. Nakayama Nanoscale, 14 14552 (2022).

3.

First-principles study of Fe2 VAl and Fe2 VAl/Si thin ﬁlms and their magnetic properties
K. Kobayashi, H. Takaki, M. Shimono, H. Ishii, N. Kobayashi, K. Hirose, and T. Mori Jpn. J.
Appl. Phys. 61 SL1013 (2022).

4.

π ‐Extended Zigzag‐Shaped Diphenanthrene‐Based p-Type Semiconductors Exhibiting SmallEﬀective Masses
M. Mitani, S. Kumagai, CP. Yu, A. Oi, M. Yamagishi, S. Nishinaga, H. Mori, Y. Nishihara, D.
Hashizume, T. Kurosawa, H. Ishii, N. Kobayashi, J. Takeya, and T. Okamoto Adv. Electron.
Mater. 8, 2200452 (2022).

KOBAYASHI, Ryo [ B class; 400 (B), 80 (C) ] (326)— Molecular dynamics analyses of ion migration at electrode-electrolyte interfaces and grain boundaries
in electrolytes
1. Na superionic conductor-type LiZr2 (PO4 )3 as a promising solid electrolyte for use in all-solidstate Li metal batteries
M. Nakayama, K. Nakano, M. Harada, N. Tanibata, H. Takeda, Y. Noda, R. Kobayashi, M.
Karasuyama, I. Takeuchi, and M. Kotobuki, Chem. Commun. 58, 9328 (2022).
DOI:10.1039/D2CC01526A
2.

Screening chloride Li‐ion conductors using high‐throughput force‐ﬁeld molecular dynamics
S. Aizu, S. Takimoto, N. Tanibata, H. Takeda, M. Nakayama, and R. Kobayashi, J. Am. Ceram.
Soc. 106, 3035 (2023).

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

DOI:10.1111/jace.18991

KOGA, Akihisa [ C class; 4200 (B), 400 (C) ] (250)— Eﬀects of ﬂux structures on Majorna excitations in Kitaev spin liquids
1. Scattering phenomena for spin transport in Kitaev spin liquid
J. Nasu, Y. Murakami, and A. Koga, Phys. Rev. B 106, 024411 (2022).
2.

Optical response of the tightbinding model on the Fibonacci chain
H. Iijima, Y. Murakami, and A. Koga J. Phys. Soc. Jpn. 91, 124702 (2022).

KOMATSU, Hisato [ B class; 300 (B), 60 (C) ] (339)— Magnetic structures on the thin ﬁlms made by the dipolar interaction and the frictional force stemmingfrom these structures
1. Transition between the stick and slip states in a simpliﬁed model of magnetic friction
H. Komatsu, Submitted to Phys. Rev. E

KOURA, Akihide [ C class; 5000 (B), 0 (C) ] ()— Machine learning study on static structure of glass materials based on
it ab initio molecular dynamics
KUNISADA, Yuji [ C class; 7800 (B), 0 (C) ] (71)
— Development of Eﬃcient Oxygen Storage Materials and Hydrogen Permeation Barrier Materials
KUROKI, Kazuhiko [ C class; 5400 (B), 0 (C) ] (194)— Theoretical studies on cuprate superconductors with quasi-one-dimensional electronic structure
1. Possibility of N-type Doping in CaAl2 Si2 -type Zintl Phase Compound CaZn2 X2 (X = As, P)
K. Nishiguchi, M. Ochi, C.H. Lee, and K. Kuroki, J. Phys. Soc. Jpn. 91, 064707 (2022).
DOI:10.7566/JPSJ.91.064707

KUSAKABE, Koichi [ C class; 3400 (B), 350 (C) ] (109)— Theoretical study of quantum eﬀects in nanographene devices
1. Spin-topological electronic valve in Ni/hBN-Graphene-hBN/Ni Magnetic Junction
Y. Wicaksono, H. Harfah, G. K. Sunnardianto, M. A. Majidi, and K. Kusakabe, Magnetochem.
9, 113 (2023).
DOI:10.3390/magnetochemistry9050113
2.

Colossal In-plane Magnetoresistance Ratio of Graphene Sandwiched with Ni Nanostructures
Y. Wicaksono, H. Harfah, G. K. Sunnardianto, M. A. Majidi, and K. Kusakabe, RSC Adv. 12,13985 (2022).
DOI:10.1039/D2RA00957A

3.

Theoretical Analysis on the Stability of 1-Pyrenebutanoic Acid Succinimidyl Ester Adsorbed on
Graphene
Y. Oishi, H. Ogi, S. Hagiwara, M. Otani, and K. Kusakabe, ACS Omega 7, 35, 31120 (2022).
DOI:10.1021/acsomega.2c03257

4.

Search for the decomposition process of 2,4,6-trinitrotoluene by an evolutionary algorithm
T. Ishikawa, K. Kusakabe, Y. Makino, S. Sakamoto, N. Okuyama, J. Phys. Chem. A 126, 8082
(2022).
DOI:10.1021/acs.jpca.2c04913

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5.

Terahertz-induced martensitic transformation in partially stabilized zirconia
M. Nagai, Y. Higashitani, M. Ashida, K. Kusakabe, H. Niioka, A. Hattori, H. Tanaka, G. Isoyama,
N. Ozaki, Commun. Phys. in press (2023)

LEE, Minhyeok [ C class; 1400 (B), 0 (C) ] (128)— Development of the Low-Temperature Oxidation Model for Alternative Fuels Using Machine Learning— Modeling of the Ammonia Decomposition Reaction on Iron-based Material Surfaces
LI, Hao [ C class; 4400 (B), 0 (C) ] (97)
— Design of CO2 Reduction Catalysts by pH-Field Dependent Simulation and New Machine LearningBased Methodology
1. The Surface States of Transition Metal X-ides under Electrocatalytic Conditions
H. Liu, X. Jia, W. Li, C. D’agostino, and H. Li, J. Chem. Phys. 158, 124705 (2023).
DOI:10.1063/5.0147123
2.

Spin-Selective Coupling in Mott–Schottky Er2 O3 -Co Boosts Electrocatalytic Oxygen Reduction
X. Wang, M. Li, P. Wang, D. Sun, L. Ding, H. Li, Y. Tang, and G. Fu, Small Methods (2023).
DOI:10.1002/smtd.202300100

3.

Design of Molecular M-N-C Dual-atom Catalysts for Nitrogen Reduction Starting from surface
state analysis
Y. Zhang, Z. Yu, F. She, L. Wei, Z. Zeng, and H. Li, J. Colloid Interface Sci. 640, 983 (2023).
DOI:10.1016/j.jcis.2023.03.033

4.

Eﬃcient Asymmetrical Silicon-Metal Dimer Electrocatalysts for Nitrogen Reduction Reaction
C. Liu, H. Zheng, T. Wang, X. Zhang, Z. Guo, and H. Li, Phys. Chem. Chem. Phys. 25, 13126(2023).
DOI:10.1039/D2CP05959B

5.

The Role of Single-Boron of N-doped Graphene for Eﬀective Nitrogen Reduction
C. Liu, T. Wang, Z. Guo, F. Zhu, G. Qin, H. Li, and S. Li, J. Mater. Sci. Technol. 159, 244(2023).
DOI:10.1016/j.jmst.2023.03.033

6.

Heterogeneous Molecular Co–N–C Catalysts for Eﬃcient Electrochemical H2 O2 Synthesis
C. Liu, Z. Yu, F. She, J. Chen, F. Liu, J. Qu, J. M. Cairney, C. Wu, K. Liu, W. Yang, H. Zheng,
Y. Chen, H. Li, and L. Wei, Energy Environ. Sci.16, 446 (2023).
DOI:10.1039/d2ee02734h7.

Improving the Oxygen Evolution Activity of Layered Double-Hydroxide via Erbium-Induced Electronic Engineering
Y. Zhu, X. Wang, X. Zhu, Z. Wu, D. Zhao, F. Wang, D. Sun, Y. Tang, H. Li, Hao and G. Fu,
Small 19, 2206531 (2023).
DOI:10.1002/smll.202206531

8.

N-doped LaPO4 : An eﬀective Pt-free catalyst for electrocatalytic oxygen reduction
R. Zhao, Z. Chen, Q. Li, X. Wang, Y. Tang, F. Fu, H. Li, J.-M. Lee, and S. Huang, Chem
Catalysis 2, 3590 (2022).
DOI:10.1016/j.checat.2022.11.008

9.

Design of Single-Atom Catalysts for Hg0 Oxidation Using H2 O2
W. Yang, X. Chen, L. Chen, Y. Feng, C. Wu, X. Ding, Z. Gao, Y. Liu, and H. Li, J. Phys. Chem.
C 126, 21234 (2022).
DOI:10.1021/acs.jpcc.2c06266

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10. Surface States of Dual-atom Catalysts Should be Considered for Analysis of Electrocatalytic
Activity
W. Yang, Z. Jia, B. Zhou, L. Wei, Z. Gao, and H. Li, Commun. Chem. 6, 6 (2023).
DOI:10.1038/s42004-022-00810-4

LIAO, YUXUAN [ C class; 4000 (B), 300 (C) ] ()— Ultimate Suppression of Nanoheat Transport with Quasi-phononic Crystal
MAEHIRA, Takahiro [ B class; 400 (B), 30 (C) ] (180)— Electronic Structure and Fermiology of d- and f-electron compounds
MAKINO, Takayuki [ B class; 400 (B), 70 (C) ] (178)— Ab-inition calculations for vibrational and magnetic properties of rare-earth monooxides with insulating ground states
MAO, WEI [ C class; 400 (B), 250 (C) ] ()— First-principles calculation of microscopic behaviors of hydrogen and Li in metal oxides
MATSUKAWA, Hiroshi [ C class; 3600 (B), 350 (C) ] ()— Physics of Friction
MATSUSHITA, Katsuyoshi [ C class; 1000 (B), 0 (C) ] (301)— Numerical Study of Collective Cell Migration in Tissue Interface— Tissue-Interface Adhesion Stabilizing Cell Motion Order
1. Collective Cell Movement under Cell-Scale Tension Gradient at Tissue Interface
K. Matsushita, H. Hashimura, H. Kuwayama, and K. Fujimoto, J. Phys. Soc. Jpn. 91, 054802(2022).
DOI:10.7566/JPSJ.91.054802
2.

Collective Cell Movement through Cell Sensing in Crossover from Suspended to Aggregated States
K. Matsushita, T. Arakaki, N. Kamamoto, M. Sudo, and K. Fujimoto, The Symposium on Traﬃc
Flow and Self-driven Particles 28, 5 (2023).

MATSUSHITA, Yu-ichiro [ E class; 7900 (B), 1250 (C) ] (65)
— Proposal for Green Devices Based on First-Principles Calculations: From Material Exploration to
Device Fabrication
1. DzyaloshinskiiMoriya interactions in Nd2 Fe14 B as the origin of spin reorientation and the rotating
magnetocaloric eﬀect
H. B. Tran and Y.-i. Matsushita, Applied Materials Today 32, 101825 (2023).
DOI:10.1016/j.apmt.2023.101825
2.

Spin property improvement of boron vacancy defect in hexagonal boron nitride by thermal treatment
T. Suzuki, Y. Yamazaki, T. Taniguchi, K. Watanabe, Y. Nishiya, Y.-i. Matsushita, K. Harii, Y.
Masuyama, Y. Hijikata, and T. Ohshima, Applied Physics Express 16, 032006 (2023).
DOI:10.35848/1882-0786/acc442

3.

Eﬀect of nitrogen introduced at the SiC/SiO2 interface and SiC side on the electronic states byﬁrst-principles calculation
K. Tachiki, Y. Nishiya, J.-I. Iwata, and Y.-i. Matsushita, arXiv:2303.08527 (2023).
DOI:10.48550/arXiv.2303.08527

401

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

4.

Atomic scale localization of Kohn-Sham wavefunction at SiO2 /4H-SiC interface under electric
ﬁeld, deviating from envelope function by eﬀective mass approximation
H. Yoshioka, J-I. Iwata, and Y-i. Matsushita, arXiv:2303.05085 (2023).
DOI:10.48550/arXiv.2303.05085

5.

First-quantized eigensolver for ground and excited states of electrons under a uniform magnetic
ﬁeld
T. Kosugi, H. Nishi, and Y-i. Matsushita, arXiv:2212.13800 (2022).
DOI:10.48550/arXiv.2212.13800

6.

Acceleration of probabilistic imaginary-time evolution method combined with quantum amplitude
ampliﬁcation
H. Nishi, T. Kosugi, Y. Nishiya, and Y-i. Matsushita, arXiv:2212.13816 (2022).
DOI:10.48550/arXiv.2212.13816

7.

Atomic and electronic structures of nitrogen vacancies in silicon nitride: Emergence of ﬂoating
gap states
F. Nanataki, K. Shiraishi, J-I. Iwata, Y-i. Matsushita, and A. Oshiyama, Phys. Rev. B 106,155201 (2022).
DOI:10.1103/PhysRevB.106.155201

8.

Exhaustive search for optimal molecular geometries using imaginary-time evolution on a quantum
computerT. Kosugi, H. Nishi, and Y. Matsushita, arXiv:2210.09883 (2022).
DOI:10.48550/arXiv.2210.09883

9.

Systematic study on the dependence of the warm-start quantum approximate optimization algorithm on approximate solutions
K. N. Okada, H. Nishi, T. Kosugi, and Y.-i. Matsushita, arXiv:2209.02942 (2022).
DOI:10.48550/arXiv.2209.02942

10. Skyrmions in van der Waals centrosymmetric materials with Dzyaloshinskii-Moriya interactions
H. B. Tran and Y.-i. Matsushita, arXiv:2209.02333 (2022).
DOI:10.48550/arXiv.2209.02333
11. Imaginary-time evolution using forward and backward real-time evolution with a single ancilla:
First-quantized eigensolver algorithm for quantum chemistry
T. Kosugi, Y. Nishiya, H. Nishi, and Y.-i. Matsushita, Phys. Rev. Research 4, 033121 (2022).
DOI:10.1103/PhysRevResearch.4.033121

MAYUMI, Koichi [ C class; 3800 (B), 0 (C) ] (258)— Mechanism of Strain-Induced Crystallization in Polymer Gels
MINAMI, Susumu [ B class; 500 (B), 90 (C) ] (169)— First-principles study of magnetic tunnel conductance and interfacial structure in topological magnets
MISAWA, Masaaki [ B class; 400 (B), 0 (C) ] (333)— Molecular dynamics study on destruction phenomena under extreme conditions
1. Surface Diﬀusion-Limited Growth of Large and High-Quality Monolayer Transition Metal Dichalcogenides in Conﬁned Space of Microreactor
H. Suzuki, R. Hashimoto, M. Misawa, Y. Liu, M. Kishibuchi, K. Ishimura, K. Tsuruta, Y. Miyata,and Y. Hayashi, ACS Nano 16, 11360 (2022).
DOI:10.1021/acsnano.2c05076

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2.

First-principles analysis of stearic acid adsorption on calcite (104) surface
N. Machida, M. Misawa, Y. Kezuka, and K. Tsuruta, e-Journal of Surface Science and Nanotechnology 20, 261 (2022).
DOI:10.1380/ejssnt.2022-041

3.

Defect-free and crystallinity-preserving ductile deformation in semiconducting Ag 2 S
M. Misawa, H. Hokyo, S. Fukushima, K. Shimamura, A. Koura, F. Shimojo, R. K. Kalia, A.
Nakano, and P. Vashishta, Sci. Rep. 12, 19458 (2022).
DOI:10.1038/s41598-022-24004-z

MITARAI, Yoko [ B class; 900 (B), 80 (C) ] (152)— Phase stability and mechanical properties of high-entropy alloys— Phase transformation of high-entropy alloys
MIZUKAMI, Wataru [ C class; 5600 (B), 500 (C) ] (233)— Simulations of stochastic quantum-classical-hybrid calculations for sensor materials with consideringnoise
1. Solvent distribution eﬀects on quantum chemical calculations with quantum computers
Y. Yoshida, W. Mizukami, and N. Yoshida, arXiv.2208.12487
DOI:10.48550/arXiv.2208.12487
2.

Hunting for quantum-classical crossover in condensed matter problems
N. Yoshioka, T. Okubo, Y. Suzuki, Y. Koizumi, and W. Mizukami, arXiv.2210.14109
DOI:10.48550/arXiv.2210.14109

MIZUSHIMA, Takeshi [ B class; 500 (B), 80 (C) ] (320)— Nonlinear dynamics of superconducting liquid crystal order
1. Manipulation of Majorana Kramers Qubit and its Tolerance in Time-Reversal-Invariant Topological Superconductor
T. Sanno, M. G. Yamada, T. Mizushima, and S. Fujimoto, Phys. Rev. B 106, 014522 (2022).
DOI:10.1103/PhysRevB.106.014522
2.

Engineering Yang-Lee anyons via Majorana bound states
Y. Tanaka, T. Sanno, T. Mizushima, and S. Fujimoto, Phys. Rev. B 106, 174517 (2022).
DOI:10.1103/PhysRevB.106.174517

MOCHIZUKI, Izumi [ B class; 500 (B), 0 (C) ] (323)— Precise structural analysis of atomic arrangement of hydrogen-adsorbed ceria surfaces by using 2DMAT
1. Two-stage data-analysis method for total-reﬂection high-energy positron diﬀraction (TRHEPD)
K. Tanaka, I. Mochizuki, T. Hanada, A. Ichimiya, T. Hyodo, and T. Hoshi, JJAP Conf. Series
9, 011301 (2023).
DOI:10.56646/jjapcp.9.0 011301
2.

Data-analysis software framework 2DMAT and its application to experimental measurements for
two-dimensional material structures
Y. Motoyama, K. Yoshimi, I. Mochizuki, H. Iwamoto, H. Ichinose, and T. Hoshi, Comp. Phys.
Commun. 280, 108465 (2022).
DOI:10.1016/j.cpc.2022.108465

3.

Data-driven Sensitivity Analysis in Surface Structure Determination by Total-Reﬂection HighEnergy Positron Diﬀraction (TRHEPD)
T. Hoshi, I. Mochizuki, S. Tanaka, T. Hyodo, and K. Hukushima Photon Factory Highlights 2021,

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46 (2022).
Data Repository
2DMAT Gallery
https://isspns-gitlab.issp.u-tokyo.ac.jp/2dmat-dev/2dmat-gallery

MOCHIZUKI, Masahito [ C class; 6200 (B), 50 (C) ] (192)— Research for photoinduced phenomena in topological magnets— Research on photoinduced topological electronic states in metallic magnets
1. Low-Energy Excitations of Skyrmion Crystals in a Centrosymmetric Kondo-Lattice Magnet: Decoupled Spin-Charge Excitations and Nonreciprocity
R. Eto, R. Pohle, and M. Mochizuki, Phys. Rev. Lett. bf 129, 017201 (2022).
DOI:10.1103/PhysRevLett.129.017201
2.

Dynamical Phase Transitions in the Photodriven Charge-Ordered Dirac-Electron System
Y. Tanaka and M. Mochizuki, Phys. Rev. Lett. bf 129, 047402 (2022).
DOI:10.1103/PhysRevLett.129.047402

MOCHIZUKI, Yasuhide [ C class; 5000 (B), 400 (C) ] (83)
— Elucidation and design principle of negative thermal expansion originating from lattice vibration
1. Chemical Aspect of Displacive-Type Ferroaxial Phase Transition from Perspective of Second-Order
Jahn-Teller Eﬀect: NASICON Systems as an Example
T. Nagai, Y. Mochizuki, S. Yoshida, and T. Kimura, J. Am. Chem. Soc. 145, 8090 (2023).
DOI:10.1021/jacs.3c00797

MORIKAWA, Yoshitada [ E class; 18000 (B), 1300 (C) ] (45)— Theoretical study on dynamical processes in heterogeneous catalysis using density functional theory
and machine learning methods
1. The Elucidation of Cu-Zn Surface Alloying on Cu(997) by Machine-Learning Molecular Dynamics
Harry Handoko Halim and Yoshitada Morikawa, ACS Physical Chemistry Au 2, 430 (2022).
DOI:10.1021/acsphyschemau.2c00017
2.

Hydrogenation of formate species using atomic hydrogen on a Cu(111) model catalyst
K. Takeyasu, Y. Sawaki, T. Imabayashi, S. E. M. Putra, H. H. Halim, J. Quan, Y. Hamamoto, I.
Hamada, Y. Morikawa, T. Kondo, T. Fujitani, and J. Nakamura J. Am. Chem. Soc. 144, 12158(2022).
DOI:10.1021/jacs.2c02797

3.

Density functional theory study of NO-H2 O co-adsorption on Cu(111)
T. N. Pham, Y. Hamamoto, K. Inagaki, I. Hamada, and Y. Morikawa, Phys. Rev. Materials 6,075801 (2022).
DOI:10.1103/PhysRevMaterials.6.075801

4.

Activity and selectivity of N2 ﬁxation on B doped g-C9 N10 : A density functional theory study
Y.-L. Wang, T. N. Pham, L.-K. Yan, and Y. Morikawa, J. Mater. Chem. C 10, 11791-11800(2022).
DOI:10.1039/D2TC02041F

MORITA, Katsuhiro [ B class; 600 (B), 0 (C) ] (316)— Finite temperature calculations of frustrated quantum spin systems

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

MORITA, Satoshi [ B class; 500 (B), 80 (C) ] (319)— Identiﬁcation of universality classes by tensor network methods
1. Non-monotonic behavior of the Binder Parameter in the systems with the Potts symmetry
H. Watanabe, Y. Motoyama, S. Morita, and N. Kawashima, Prog. Theor. Exp. Phys. 2023,
033A02 (2023).
DOI:10.1093/ptep/ptad022
2.

Ground-state phase diagram of a spin-1/2 frustrated XXZ ladder
T. Ogino, R. Kaneko, S. Morita, and S. Furukawa, Phys. Rev. B 106, 155106 (2022).
DOI:10.1103/PhysRevB.106.155106

3.

TeNeS: Tensor Network Solver for Quantum Lattice Systems
Y. Motoyama, T. Okubo, K. Yoshimi, S. Morita, T. Kato, and N. Kawashima, Comput. Phys.
Commun. 279, 108437 (2022).
DOI:10.1016/j.cpc.2022.108437

MOTOME, Yukitoshi [ C class; 12800 (B), 950 (C) ] (190)— Theoretical study of strongly-correlated topological phenomena by exploiting ﬁrst-principles calculations and machine learning
1. Inter-edge spin resonance in the Kitaev quantum spin liquid
T. Misawa, J. Nasu, and Y. Motome submitted to Phys. Rev. B
2.

Ground-State Phase Diagram of the Kitaev-Heisenberg Model on a Three-dimensional Hyperhoneycomb Lattice
K. Fukui, Y. Kato, and Y. Motome, submitted to J. Phys. Soc. Jpn.

3.

Thermally-robust spatiotemporal parallel reservoir computing by frequency ﬁltering in frustrated
magnets
K. Kobayashi and Y. Motome, submitted to Nature Commun.

4.

One, two, three, … inﬁnity: topological properties of thin ﬁlms of Co-based shandite
K. Nakazawa, Y. Kato, and Y. Motome submitted to Commun. Phys.

5.

Spin nematics meet spin liquids: Exotic quantum phases in the spin-1 bilinear-biquadratic modelwith Kitaev interactions
R. Pohle, N. Shannon, and Y. Motome, Phys. Rev. B. 107, L140403 (2023).
DOI:10.1103/PhysRevB.107.L140403

6.

Hidden topological transitions in emergent magnetic monopole lattices
Y. Kato and Y. Motome, Phys. Rev. B. 107, 094437 (2023).
DOI:10.1103/PhysRevB.107.094437

7.

Inverse Hamiltonian design by automatic diﬀerentiation
K. Inui and Y. Motome Commun. Phys. 6, 37 (2023).
DOI:10.1038/s42005-023-01132-0

8.

Anomalous Hall eﬀect in antiferromagnetic perovskites
M. Naka, Y. Motome, and H. Seo Phys. Rev. B. 106, 195149 (2022).
DOI:10.1103/PhysRevB.106.195149

9.

Ground-state phase diagram of spin-S Kitaev-Heisenberg models
K. Fukui, Y. Kato, J. Nasu, and Y. Motome, Phys. Rev. B. 106, 174416 (2022).
DOI:10.1103/PhysRevB.106.174416

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10. Magnetic Hedgehog Lattice in a Centrosymmetric Cubic Metal
S. Okumura, S. Hayami, Y. Kato, and Y. Motome, J. Phys. Soc. Jpn. 91, 093702 (2022).
DOI:10.7566/JPSJ.91.093702
11. Feasibility of Kitaev quantum spin liquids in ultracold polar molecules
K. Fukui, Y. Kato, J. Nasu, and Y. Motome, Phys. Rev. B 106, 014419 (2022).
DOI:10.1103/PhysRevB.106.014419
12. Phase degree of freedom and topology in multiple-Q spin textures
K. Shimizu, S. Okumura, Y. Kato, and Y. Motome, Phys. Rev. B 105, 224405 (2022).
DOI:10.1103/PhysRevB.105.224405
13. Magnetic ﬁeldtemperature phase diagrams for multiple-Q magnetic ordering: Exact steepest descent approach to long-range interacting spin systems
Y. Kato and Y. Motome Phys. Rev. B 105, 174413 (2022).
DOI:10.1103/PhysRevB.105.174413
14. Asymmetric modulation of Majorana excitation spectra and nonreciprocal thermal transport in
the Kitaev spin liquid under a staggered magnetic ﬁeld
K. Nakazawa, Y. Kato, and Y. Motome Phys. Rev. B 105, 165152 (2022).
DOI:10.1103/PhysRevB.105.165152

MURASHIMA, Takahiro [ C class; 4800 (B), 400 (C) ] (241)— Polymer Dynamics under Elongational Flow
1. Topological transition in multicyclic chains with structural symmetry inducing stress-overshootphenomena in multicyclic/linear blends under biaxial elongational ﬂow
T. Murashima, K. Hagita, and T. Kawakatsu, Macromolecules 55, 9358 (2022).
DOI:10.1021/acs.macromol.2c01579

NADA, Hiroki [ B,C class; 700 (B), 400 (C) ] (288, 289)
— Analysis of Various Crystal Forms of Glycine Nanocrystals by Large-Scale Metadynamics Simulations— Structural Analysis of Amorphous Silicon Monoxide by a Metadynamics Method with Unsupervised
Machine Learning
1. Stable Binding Conformations of Polymaleic and Polyacrylic Acids at a Calcite Surface in the
Presence of Countercations: A Metadynamics Study
H. Nada, Langmuir 38 (2022) 7046.
DOI:10.1021/acs.langmuir.2c00750
2.

Eﬀect of nitrogen molecules on the growth kinetics at the interface between a (111) plane of cubic
ice and water
H. Nada, J. Chem. Phys. 157 (2022) 124701.
DOI:10.1063/5.0106842

3.

Cinematographic recording of metastable ﬂoating island in 2-D and 3-D crystal growth
M. Sakakibara, H. Nada, T. Nakamuro, E. Nakamura ACS Central Science 8 (2022) 1704.
DOI:10.1021/acscentsci.2c01093

4.

Formation of Hydrogen-Ordered Cubic Ice Polymorphs via Annealing of Ice Ic at 100-130 K
A. Kouchi1, T. Yamazaki, H. Katsuno, H. Nada, T. Hama, Y. Kimura Submitted to Chem. Phys.

NAKAGAWA, Naoko [ C class; 3800 (B), 350 (C) ] (252)— Nontrivial properties of interface and ﬁnite size eﬀects of free energies

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

1.

Work relation for determining the mixing free energy of small-scale mixtures
A. Yoshida and N. Nakagawa, Phys. Rev. Res. 4, 023119 (2022).
DOI:10.1103/PhysRevResearch.4.023119

2.

微小溶液の混合自由エネルギーを決定する仕事関係式 – ジャルジンスキー等式・錬金術的操作・分子の識別可能性
吉田旭、中川尚子、日本物理学会誌 78, 140 (2023).

NAKAMURA, Kazuma [ C class; 2000 (B), 0 (C) ] (122)— Ab initio calculations for stability of multilayer sysmte— Ab initio calculations for structural stability and property of multilayer system
1. Hidden Markov model analysis for ﬂuorescent time series of quantum dots
T. Furuta, K. Hamada, M. Oda, K. Nakamura, Phys. Rev. B. 106, (2022) 104305.

NAKANO, Hiroki [ C class; 3000 (B), 250 (C) ] (261)— Numerical study on low-energy states of quantum spin systems
1. The Haldane Gap of the S = 1 Heisenberg Antiferromagnetic Chain
H. Nakano, H. Tadano, N. Todoroki, and T. Sakai, J. Phys. Soc. Jpn. 91, 074701 (2022).
DOI:10.7566/JPSJ.91.074701

NAKANO, Hiroyoshi [ C class; 7800 (B), 350 (C) ] (221)— Nonequilibrium long-range correlation in heat conduction systems— Numerical analysis of motility-induced phase transition in active Brownian particle system
NAKAYAMA, Akira [ C class; 2400 (B), 0 (C) ] (119)— First-principles molecular dynamics study for metal-oxide catalysis
NAKAYAMA, Masanobu [ C class; 1000 (B), 0 (C) ] (299)— Unveiling grain boundary alkaline ion conductivity by using materials simulation and
1. Materials Simulation for the Modulated Arrangement in A-Site Deﬁcient Perovskite-Type Lithium
Doped Lanthanum Niobate as Solid Electrolytes for All Solid-State Li Ion Batteries
Z. Yang and M. Nakayama, J. Phys. Soc. Jpn. 91, 091010 (2022).
DOI:10.7566/JPSJ.91.091010
2.

Na superionic conductor-type LiZr2 (PO4 )3 as a promising solid electrolyte for use in all-solid-state
Li metal batteries
M. Nakayama, K. Nakano, M. Harada, N. Tanibata, H. Takeda, Y. Noda, R. Kobayashi, M.
Karasuyama, I. Takeuchi, and M. Kotobukii , Chem. Commun. 58, 9328 (2022).
DOI:10.1039/D2CC01526A

3.

Bayesian optimisation with transfer learning for NASICON-type solid electrolytes for all-solidstate Li-metal batteries
H. Fukuda, S. Kusakawa, K. Nakano, N. Tanibata, H. Takeda, M. Nakayama, M. Karasuyama, I.
Takeuchi, T. Natori, Y. Ono, and T. Natori, RSC Adv. 12, 30696 (2022).
DOI:10.1039/D2RA04539G

4.

Screening chloride Li-ion conductors using high-throughput force-ﬁeld molecular dynamics
S. Aizu, S. Takimoto, N. Tanibata, H. Takeda, M. Nakayama, and R. Kobayashi , J. Am. Ceram.
Soc., 106, 4045 (2023).
DOI:10.1111/jace.18991

5.

Reaction Kinetics of Carbonation at the Surface of Garnet-Type Li7 La3 Zr2 O12 as Solid Elec-407

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

trolytes for All-Solid-State Li Ion Batteries
M. Nakayama, T. Horie, R. Natsume, S. Hashimura, N. Tanibata, H. Takeda, H. Maeda, and M.
Kotobuki, J. Phys. Chem. C 127, 7595 (2023).
DOI:10.1021/acs.jpcc.2c08588
6.

Bayesian optimisation with transfer learning for NASICON-type solid electrolytes for all-solidstate Li-metal batteries
H. Fukuda, S. Kusakawa, K. Nakano, N. Tanibata, H. Takeda, M. Nakayama, M. Karasuyama, I.
Takeuchi, T. Natori, Y. Ono, and T. Natori, RSC Adv. 12, 30696 (2022).
DOI:10.1039/D2RA04539G

NAKAYAMA, Takashi [ C class; 4600 (B), 400 (C) ] (89)— Theory of tunneling currents at semiconductor pn junctions in electric ﬁelds based on ﬁrst-principles
calculation
1. Enhancement of tunneling currents by isoelectronic nitrogen-atom doping at semiconductor pnjunctions; comparison of indirect and direct band-gap systems
S. Cho and T. Nakayama, Jpn. J. Appl. Phys. 61, 124002 (2022).
DOI:10.35848/1347-4065/ac9fb0
2.

Photo-carrier induced composition separation in mixed-halide CsPb(Ix Br1−x )3 perovskite semiconductors; ﬁrst-principles calculation
A. Tomita and T. Nakayama, Appl. Phys. Express,16, 041002 (2023).
DOI:10.35848/1882-0786/acc6b3

NASU, Joji [ C class; 3200 (B), 250 (C) ] (201)— Magnetic ﬁeld eﬀect on excitonic phase in multi-orbital Hubbard model
1. Antisymmetric Thermopolarization by Electric Toroidicity
J. Nasu and S. Hayami, Phys. Rev. B 105, 245125 (2022).
DOI:10.1103/PhysRevB.105.245125
2.

Scattering phenomena for spin transport in Kitaev spin liquid
J. Nasu Y. Murakami, and A. Koga, Phys. Rev. B 106, 024411 (2022).
DOI:10.1103/PhysRevB.106.024411

3.

Feasibility of Kitaev quantum spin liquids in ultracold polar molecules
K. Fukui, Y. Kato, J. Nasu and Y. Motome, Phys. Rev. B 106, 014419 (2022).
DOI:10.1103/PhysRevB.106.014419

4.

Ground-state phase diagram of spin-S Kitaev-Heisenberg models
K. Fukui, Y. Kato, J. Nasu, and Y. Motome, Phys. Rev. B 106, 174416 (2022).
DOI:10.1103/PhysRevB.106.174416

NIKI, Kaori [ C class; 1000 (B), 0 (C) ] (144)— Analysis of wavenumber space-resolved photoelectron spectrum of molecules adsorbed on surface
1. Adsorbed CO2 Mediated CO2 Photoconversion Cycle into Solar Fuel at the O Vacancy Site of
Zirconium Oxide
K. Hara, M. Nozaki, R. Hirayama, R. Ishii, K. Niki, and Y. Izumi, J. Phys. Chem. C 127, 1776
(2023).
DOI:10.1021/acs.jpcc.2c06048

408

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of TokyoNISHIDATE, Kazume [ C class; 800 (B), 0 (C) ] (158)— Electronic structure investigation of the Ba2 PrBiO6 photocatalyst
1. Enhanced photocatalytic activities under visible light of double-perovskite oxide semiconductor
Ba2 Tb(Bi, Sb)O6 with mixed-valence
D. C. Roy, M. Matsukawa, T. Yonai, M. Arakida, H. Taniguchi, K. Nishidate, S. Aisawa, A.
Matsushita, and L. Shiqi, J. Mater. Sci. Mater. Electron. 34 (2023) 281.
DOI:10.1007/s10854-022-09542-6
2.

Gap opening at the Dirac point of graphene on Cu(111): Hybridization versus sublattice symmetry
breaking
K. Nishidate, M. Matsukawa, and M. Hasegawa, Surface Science, 728, 122196 (2023).
DOI:10.1016/j.susc.2022.122196

NISHIGUCHI, Kazutaka [ B class; 400 (B), 70 (C) ] (210)— Theoretical study of thermoelectric properties in Heusler compounds: A weak-coupling approach
1. Characterization of Planar Defect in Layered Perovskite Photocatalyst Y 2 Ti2 O5 S2 by Electron
Microscopy and First-Principles Calculations
M. Nakabayashi, K. Nishiguchi, X. Liang, T. Hisatomi, T. Takata, T. Tsuchimochi, N. Shibata,K. Domen, and S. L. Ten-no, J. Phys. Chem. C (in press).
DOI:10.1021/acs.jpcc.3c00820

NISHIKAWA, Yoshihiko [ C class; 6800 (B), 500 (C) ] ()— Numerical study of thermodynamic properties of a lattice glass model
NOGUCHI, Hiroshi [ C class; 6200 (B), 500 (C) ] (35)— structure formation of biomembrane
1. Conformations of three types of ultra-long-chain fatty acids in multi-component lipid bilayers
K. Kawaguchi, H. Nagao, H. Shindou, and H. Noguchi J. Phys. Chem. B 126, 9316-9324 (2022).
DOI:10.1021/acs.jpcb.2c06189
2.

Eﬀects of gas-liquid phase transitions on soundwave propagation: A molecular dynamics study
Y. Asano, H. Watanabe, and H. Noguchi Phys. Rev. Fluids 7, 064302 (2022).
DOI:10.1103/PhysRevFluids.7.064302

NOGUCHI, Yoshifumi [ C class; 3800 (B), 350 (C) ] (106)— Investigation on optical properties of TADF molecules
1. Development of the Bethe-Salpeter method considering second-order corrections for a GW electronhole interaction kernel
S. Yamada, Y. Noguchi, K. Ishii , D. Hirose, O. Sugino, and K. Ohno, Phys. Rev. B. 106, 045113(2022).
DOI:10.1103/PhysRevB.106.045113

NOMURA, Yusuke [ C class; 4200 (B), 400 (C) ] (196)— Study on nonlocal correlations in multi-orbital strongly-correlated materials
1. Ab initio materials design of superconductivity in d9 nickelates
M. Kitatani, Y. Nomura, M. Hirayama, and R. Arita APL Materials 11, 030701 (2023).
2.

Doping Asymmetry and Layer-Selective Metal-Insulator Transition in Trilayer K 3+x C6 0
C. Yue, Y. Nomura, and P. Werner Phys. Rev. Lett. 129, 066403 (2022).

3.

Fermi Surface Expansion above Critical Temperature in a Hund Ferromagnet

409

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Y. Nomura, S. Sakai, and R. Arita Phys. Rev. Lett. 128, 206401 (2022).
4.

Investigating Network Parameters in Neural-Network Quantum States
Y. Nomura J. Phys. Soc. Jpn. 91, 054709 (2022).

NONOMURA, Yoshihiko [ C class; 3600 (B), 350 (C) ] (254)— Nonequilibrium relaxation temperature scaling: Applications to quantum systems
NOZAWA, Kazuki [ C class; 1400 (B), 200 (C) ] (126)— First-principles study of surface atomic structure and chemical properties of intermetallic compounds
1. First-principles study of the initial stage of Pentacene adsorption on the twofold surface of the
Ag-In-Yb quasicrystal
M. Sato, T. Hiroto, Y. Matsushita, and K. Nozawa, J. Phys.: Conf. Ser. 2461 012016 (2023).
DOI:10.1088/1742-6596/2461/1/012016

OBATA, Masao [ B class; 800 (B), 140 (C) ] (153)— Analysis of magnetic material with an anisotropic crystal structure— Analysis of magnetic materials with anisotropic crystal structures
1. Intrinsic instability to martensite phases in ferromagnetic shape memory alloy Ni 2 MnGa: Quasiparticle self-consistent GW investigation
M. Obata, T. Kotani, and T. Oda, Phys. Rev. Materials 7, 024413 (2023).
DOI:10.1103/PhysRevMaterials.7.024413

OCHI, Masayuki [ C class; 4600 (B), 0 (C) ] (94)
— Development of a ﬁrst-principles calculation method using many-body wave functions
1. Chemical Pressure Eﬀect on Structural and Physical Properties of 15R-SrVO2.2 N0.6 with AnionVacancy OrderK. Murayama, H. Takatsu, M. Ochi, M. Namba, K. Kuroki, and H. Kageyama J. Phys. Soc. Jpn.
91, 064805 (2022).
DOI:10.7566/JPSJ.91.064805
2.

Topochemical Synthesis of Ca3 CrN3 H Involving a Rotational Structural Transformation for Catalytic Ammonia Synthesis
Y. Cao, M. A. Kirsanova, M. Ochi, W. A. Maksoud, T. Zhu, R. Rai, S. Gao, T. Tsumori, S.
Kobayashi, S. Kawaguchi, E. Abou-Hamad, K. Kuroki, C. Tassel, A. M. Abakumov, Y. Kobayashi,
and H. Kageyama Angew. Chem. Int. Ed. 61, e202209187 (2022).
DOI:10.1002/anie.20220918

3.

Superconductivity in a Magnetic Rashba Semimetal EuAuBi
H. Takahashi, K. Akiba, M. Takahashi, A. H. Mayo, M. Ochi, T. C. Kobayashi, and S. Ishiwata
J. Phys. Soc. Jpn. 92, 013701 (2023).
DOI:10.7566/JPSJ.92.013701

4.

Field-tunable Weyl points and large anomalous Hall eﬀect in the degenerate magnetic semiconductor EuMg2 Bi2
M. Kondo, M. Ochi, R. Kurihara, A. Miyake, Y. Yamasaki, M. Tokunaga, H. Nakao, K. Kuroki,
T. Kida, M. Hagiwara, H. Murakawa, N. Hanasaki, and H. Sakai Phys. Rev. B 107, L121112(2023).
DOI:10.1103/PhysRevB.107.L121112

5.

TC++: First-principles calculation code for solids using the transcorrelated method
M. Ochi Comput. Phys. Commun. 287, 108687 (2023).

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

DOI:10.1016/j.cpc.2023.108687

ODA, Tatsuki [ C class; 5200 (B), 800 (C) ] (77)
— Analyses on electronic and magnetic structure and parallelization development in quasi-particle selfconsistent GW code
1. Intrinsic instability to martensite phases in ferromagnetic shape memory alloy Ni 2 MnGa: Quasiparticle self-consistent GW investigation
M. Obata, T. Kotani, and T. Oda, Phys. Rev. Mater. 7 (2023) 024413.

OHKUBO, Yuji [ B class; 900 (B), 0 (C) ] (154)— Clariﬁcation of atomistic mechanism application of process design for adhesion interface between
metal and plasma-treated ﬂuoropolymers using ﬁrst principles calculation
OHMURA, Satoshi [ C class; 1000 (B), 0 (C) ] (143)— Ab initio molecular-dynamics study of structural and transport properties of liquid mixtures containinghydrocarbon under ultra-high pressure
1. Ab Initio Molecular-Dynamics Study of Structural and Bonding Properties of Liquid Fe–Light–
Element–O Systems Under High Pressure
S. Ohmura, F. Shimojo, and T. Tsuchiya, Front. Earth Sci. 10, 873088 (2022)
DOI:10.3389/feart.2022.873088
2.

Development of a Multitimescale Time-Resolved Electron Diﬀraction Setup: Photoinduced Dynamics of Oxygen Radicals on Graphene Oxide
Y. Saida, R. Shikata, K. En-ya, S. Ohmura, Y. Nishina, and M. Hada J. Phys. Chem. A 126,
6301 (2022).
DOI:10.1021/acs.jpca.2c04075

OHNISHI, Masato [ C class; 4400 (B), 500 (C) ] (91)
— Development of Thermal Materials Using Phonon Database
1. Anharmonic phonon renormalization and thermal transport in the type-I Ba8Ga16Sn30 clathratefrom ﬁrst principles
M. Ohnishi, T. Tadano, S. Tsuneyuki, and J. Shiomi, Phys. Rev. B 106, 24303 (2022).
DOI:10.1103/PhysRevB.106.024303

OHNO, Kaoru [ C class; 4200 (B), 400 (C) ] (93)— Improvement and application of all-electron mixed basis program
1. Electronic structure of Li+ @C60 adsorbed on methyl-ammonium lead iodide perovskite CH3 NH3 PbI3
surfaces
K.-H. Chew, R. Kuwahara, and K. Ohno, Mater. Adv. 3, 290-299 (2022).
DOI:10.1039/D1MA00741F
2.

Electronic Structures of Group IIIV Element Haeckelite Compounds: A Novel Family of Semiconductors, Dirac Semimetals, and Topological Insulators
M. Khazaei, A. Ranjbar, Y.-G. Kang, Y. Liang, R. Khaledialidusti, S. Bae, H. Raebiger, V.
Wang, M. J. Han, H. Mizoguchi, M. S. Bahramy, T. D. Kühne, R. V. Belosludov, K. Ohno, andH. Hosono, Adv. Functional Mater. 32, 2110930 (2022).
DOI:10.1002/adfm.202110930

3.

Allproportional solid solution versus twophase coexistence in the TiV alloy by ﬁrstprinciples phaseﬁeld and SQS methods
K. Ohno, R. Kuwahara, T. N. Pham, S. Bhattacharyya, and R. Sahara, Sci. Rep. 12, 10070

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo(2022).
DOI:10.1038/s41598-022-13906-7
4.

Extended quasiparticle approach to non-resonant and resonant X-ray emission spectroscopy
K. Ohno and T. Aoki, Phys. Chem. Chem. Phys. 24, 16586 (2022).
DOI:10.1039/D2CP00988A

5.

Development of the Bethe-Salpeter method considering second-order corrections for a GW electronhole interaction kernel
S. Yamada, Y. Noguchi, K. Ishii, D. Hirose, O. Sugino, and K. Ohno, Phys. Rev. B 106, 045113(2022).
DOI:10.1103/PhysRevB.106.045113

6.

Position dependent dielectric function near the Cu surface
Y. Sakamoto, K. Fujii, S. Nakamura, and K. Ohno, J. Phys. Soc. Jpn. (Lett.) 91, 113703 (2022).
DOI:10.7566/JPSJ.91.113703

7.

Electronic structure analysis of light element-doped anatase TiO2 using all-electron GW approach
T. Ishikawa, R. Sahara, K. Ohno, K. Ueda, and T. Narushima, Comp. Mat. Sci. 220, 112059(2023).
DOI:10.1016/j.commatsci.2023.112059

8.

Microstructures in iron-rich FeSi alloys by ﬁrst-principles phase ﬁeld and special quasirandom
structure methods
K. Ohno, R. Kuwahara, R. Sahara, T. N. Pham, S. Bhattacharyya, Y. Kawaoze, and K. Fujisaki,
ISIJ International 63, 553 (2023).
DOI:10.2355/isijinternational.ISIJINT-2022-465

OHSAWA, Kazuhito [ C class; 1000 (B), 0 (C) ] (141)— Study of interaction between radiation damage and interstitial atom
OHTO, Tatsuhiko [ C class; 3000 (B), 0 (C) ] (114)— First-principles study on the structure of water/graphene interfaces depending on the number ofgraphene layers
1. Controlling the Emissive, Chiroptical, and Electrochemical Properties of Double [7] Helicenes
through Embedded Aromatic Rings
J. Hong, X. Xiao, H. Liu, E. Dmitrieva, A. A. Popov, Z. Yu, M.-D. Li, T. Ohto, J. Liu, A. Narita,
P. Liu, H. Tada, X.-Y. Cao, X.-Y. Wang, Y. Zou, K. Mullen, and Y. Hu Chem. Eur. J. 28,
e202202243 (2022).
2.

The dielectric function proﬁle across the water interface through surface-speciﬁc vibrational spectroscopy and simulations
K.-Y. Chiang, T. Seki, C.-C. Yu, T. Ohto, J. Hunger, M. Bonn, and Y. Nagata Proc. Natl. Acad.
Sci. USA 119, e2204156119 (2022).

3.

Coexistence of Urbach-tail-like localized states and metallic conduction channels in nitrogen-doped
3D curved graphene
Y. Tanabe, Y. Ito, K. Sugawara, S. Jeong, T. Ohto, T. Nishiuchi, N. Kawada, S. Kimura, C. F.
Aleman, T. Takahashi, M. Kotani, and M. Chen Adv. Mater. 34, 2205986 (2022).

4.

Corrosion-resistant and high-entropic non-noble-metal electrodes for oxygen evolution in acidic
media
A. A. H. Tajuddin, M. Wakisaka, T. Ohto, Y. Yu, H. Fukushima, H. Tanimoto, X. Li, Y. Misu,
S. Jeong, J.-i. Fujita, H. Tada, T. Fujita, M. Takeguchi, K. Takano, K. Matsuoka, Y. Sato, and412

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Y. Ito Adv. Mater. 35, 2207466 (2023).

OHTSUKI, Tomi [ C class; 5600 (B), 200 (C) ] (239)— Localization-delocalization transition in non-Hermitian random system
1. Deciphering quantum ﬁngerprints in electric conductance
Daimon, K. Tsunekawa, S. Kawakami, T. Kikkawa, R. Ramos, K. Oyanagi, T. Ohtsuki, and E.
Saitoh Nat. Commun. 13, 3160 (2022).
2.

Universality classes of the Anderson transitions driven by quasiperiodic potential in the threedimensional Wigner-Dyson symmetry classes
X. Luo and T. Ohtsuki Phys. Rev. B 106, 104205 (2022)

3.

Unifying the Anderson Transitions in Hermitian and Non-Hermitian Systems
X. Luo, Z. Xiao, K. Kawabata, T. Ohtsuki, and R. Shindou Phys. Rev. Res. 4, L022035 (2022).

4.

Topological Anderson Transitions in Chiral Symmetry Classes
Z Xiao, K Kawabata, X Luo, T Ohtsuki, and R Shindou arXiv:2211.09999

5.

Irrelevant Corrections at the Quantum Hall Transition
K. Slevin and T. Ohtsuki Phys. Status Solidi RRL 2023, 2300080 (2023).

OKUBO, Masashi [ B class; 1100 (B), 170 (C) ] (132)— Charge storage mechanism of layered transition metal carbides with regulated terminal groups— Exploration of transition-metal carbides and nitrides
OKUBO, Tsuyoshi [ C class; 6200 (B), 500 (C) ] (228)— Novel phenomena in frustrated magnets
1. Hunting for quantum-classical crossover in condensed matter problems
N. Yoshioka, T. Okubo, Y. Suzuki, Y. Koizumi, and W. Mizukami, arXiv:2210.14109
DOI:10.48550/arXiv.2210.14109
2.

TeNeS: Tensor Network Solver for Quantum Lattice Systems
Y. Motoyama, T. Okubo, K. Yoshimi, S. Morita, T. Kato, and N. Kawashima, Comput. Phys.
Commun. 279, 108437 (2022).
DOI:10.1016/j.cpc.2022.108437

3.

拡張キタエフ模型における熱ホール伝導度のテンソルネットワーク法による研究
大久保毅、固体物理 57, 633 (2022).

OKUMURA, Hisashi [ C class; 3800 (B), 350 (C) ] (251)— Molecular dynamics simulation of disease-related biomolecules
1. Molecular Dynamics Simulation Studies on the Aggregation of Amyloid-β Peptides and Their
Disaggregation by Ultrasonic Wave and Infrared Laser Irradiation
H. Okumura and S. G. Itoh, Molecules 27, 2483 (2022).
DOI:10.3390/molecules27082483
2.

Key residue for aggregation of amyloid-β peptides
S. G. Itoh, M. Yagi-Utsumi, K. Kato, and H. Okumura, ACS Chem. Neurosci. 13, 31393151(2022).
DOI:10.1021/acschemneuro.2c00358

413

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

OKUMURA, Masahiko [ B class; 500 (B), 90 (C) ] (317)— Machine learning molecular dynamics studies of complex structural materials
ONO, Atsushi [ B class; 200 (B), 80 (C) ] (212)— Photoinduced nonequilibrium dynamics in itinerant magnets
1. Energy-band echoes: Time-reversed light emission from optically driven quasiparticle wave packets
S. Imai, A. Ono, and S. Ishihara, Phys. Rev. Res. 4, 043155 (2022).
DOI:10.1103/PhysRevResearch.4.043155

ONO, Shota [ B class; 900 (B), 130 (C) ] (140)— Metastable structure map of Lennard-Jones cluster based on vibrational frequency analysis— Stable structure of 2D metals on graphene substrate— Surface Alloy Database
1. Optimization of conﬁgurations of atomic species on two-dimensional hexagonal lattices for copperbased systems
S. Ono, AIP Adv. 12, 085313 (2022).
DOI:10.1063/5.0098517
2.

Two-Dimensional Ionic Crystals: The Cases of IA-VII Alkali Halides and IA-IB CsAu
S. Ono, J. Phys. Soc. Jpn. 91, 094606 (2022).
DOI:10.7566/JPSJ.91.094606

3.

Small atoms fall into bulk from non-close-packed metal surfaces?
S. Ono, H. Satomi, and J. Yuhara, Comp. Mater. Sci. 218, 111959 (2023).
DOI:10.1016/j.commatsci.2022.111959

ONO, Tomoya [ C class; 12800 (B), 1000 (C) ] (53)
— Development of ﬁrst-principles calculation code RSPACE and design of highly functional interface
1. Density functional theory calculations for investigation of atomic structures of 4H-SiC/SiO 2 interface after NO annealing
N. Komatsu, M. Ohmoto, M. Uemoto, and T. Ono, J. Appl. Phys. 132, 155701 (2022).
2.

Density functional study of twisted graphene L10 -FePd heterogeneous interface
M. Uemoto, H. Adachi, H. Naganuma and T. Ono, J. Appl. Phys. 132, 095301 (2022).

3.

Unique electrical signature of phosphate for speciﬁc single-molecule detection of peptide phosphorylation
T. Harashima, Y. Egami, K. Homma, Y. Jono, S. Kaneko, S. Fujii, T. Ono, and T. Nishino, J.
Am. Chem. Soc. 144, 17449 (2022).

OSHIKAWA, Masaki [ B class; 600 (B), 90 (C) ] (314)— Gapless symmetry-protected topological phase in anisotropic triangle strips
1. Gapless symmetry-protected topological phase of quantum antiferromagnets on anisotropic triangular strip
Y. Hidaka, S. C. Furuya, A. Ueda, and Y. Tada, Phys. Rev. B 106, 144436 (2022).
DOI:10.1103/PhysRevB.106.144436
2.

Tensor network renormalization study on the crossover in classical Heisenberg and RP 2 models in
two dimensions
A. Ueda and M. Oshikawa Phys. Rev. E 106, 014104 (2022).
DOI:10.1103/PhysRevE.106.014104

414

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

OSHIYAMA, Atsushi [ E class; 16500 (B), 1200 (C) ] (48)— Clariﬁcation of Microscopic Mechanisms of Semiconductor Epitaxial Growth and Device-Interface
Formation by Large-Scale Quantum-Theory-Based Computations
1. An atomistic insight into reactions and free-energy proﬁles of NH3 and Ga on GaN surfaces during
the epitaxial growth
M. Boero, K. M. Bui, K. Shiraishi, K. Ishisone, Y. Kangawa, and A. Oshiyama, Appl. Surf. Sci.
599, 153935 (2022).
2.

Microscopic identiﬁcation of stepped SiC (0001) and the reaction site of hydrogen-rich epitaxial
growthT. Kimura, K. Chokawa, K. Shiraishi, and A. Oshiyama, Phys. Rev. B 106, 035309 (2022).

3.

Chickens or Eggs in the Atomic World: Structures and Electronic Properties of Defects in Semiconductors
A. Oshiyama JPSJ News and Comments 19, 12 (2022).

4.

Atomistic insight into initial stage of graphene formation on SiC (0001) surfaces
M. Boero, F. Imoto and A. Oshiyama, Phys. Rev. Materials 6, 093403 (2022).

5.

Atomic and electronic structures of nitrogen vacancies in silicon nitride: Emergence of ﬂoating
gap states
F. Nanataki, K. Shiraishi, J.-I. Iwata, Y.-i. Matsushita, and A. Oshiyama, Phys. Rev. B 106,155201 (2022).

6.

Insight into the Step Flow Growth of Gallium Nitride based on Density Functional Theory
K. M. Bui, K. Shiraishi and A. Oshiyama, Appl. Surf. Sci. 613, 155840 (2023).

7.

Atomic and electronic structures of interfaces between amorphous (Al2 O3 )1−x (SiO2 )x and GaN
polar surfaces revealed by ﬁrst-principles simulated annealing techniqueK. Chokawa, K. Shiraishi and A. Oshiyama, J. Appl. Phys 133, 065301 (2023).

8.

Microscopic physical origin of charge traps in 3D NAND ﬂash memories
F. Nanataki, J.-i. Iwata, K. Chokawa, M. Araidai, A. Oshiyama and K. Shiraishi, Jpn. J. Appl.
Phys. 62, SC1038 (2023).

OTANI, Minoru [ C class; 5000 (B), 0 (C) ] (87)
— First-principles simulation of interfacial reactions using density functional theory and classical liquid
theory1. Theoretical Consideration of Side Reactions between the VS4 Electrode and Carbonate Solventsin Lithiummetal Polysulﬁde Batteries
S. Hagiwara、J. Haruyama, M. Otani, Y. Umemura, T. Takeuchi, and H. Sakaebe, Electrochemistry 90, 107002 (2022).
DOI:10.5796/electrochemistry.22-00087
2.

Development of a dielectrically consistent reference interaction site model combined with thedensity functional theory for electrochemical interface simulations
S. Hagiwara, S. Nishihara, F. Kuroda, and M. Otani, Phys. Rev. Mater. 6, 093802 (2022).
DOI:10.1103/PhysRevMaterials.6.093802

3.

Theoretical Analysis on the Stability of 1-Pyrenebutanoic Acid Succinimidyl Ester Adsorbed on
Graphene
Y. Oishi, H. Ogi, S. Hagiwara, M. Otani, and K. Kusakabe, ACS Omega, bf 7, 31120 (2022).
DOI:10.1021/acsomega.2c03257

415

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of TokyoOTSUKI, Junya [ B class; 500 (B), 90 (C) ] ()— Dynamical mean-ﬁeld study of rare-earth ferromagnetic compounds
OTSUKI, Michio [ C class; 200 (B), 0 (C) ] (350)— Shape dependence of macroscopic friction between solids
1. Static friction coeﬃcient depends on the external pressure and block shape due to precursor slip
W. Iwashita, H. Matsukawa, and M. Otsuki, Sci. Rep. 13, 2511 (2023).
DOI:10.1038/s41598-023-29764-w

OYA, Yutaka [ C class; 3200 (B), 300 (C) ] (260)— Molecular dynamics simulation for chemical reactions in multicomponent systems of thermosetting
resinsOZAKI, Taisuke [ C class; 6200 (B), 500 (C) ] (72)
— Computational materials discovery based on densest ternary sphere packings
1. Diverse densest ternary sphere packings
R. Koshoji and T. Ozaki J. Phys. Commun. 6, 075002 (2022).
DOI:10.1088/2399-6528/ac7d38
2.

Prediction of quaternary hydrides based on densest ternary sphere packings
R. Koshoji, M. Fukuda, M. Kawamura and T. Ozaki Phys. Rev. Mater. 6, 114802 (2022).
DOI:10.1103/PhysRevMaterials.6.114802

OZEKI, Yukiyasu [ C class; 6000 (B), 0 (C) ] (238)— Development and applications of scaling analysis for critical phenomena— Gauss process regression applied to relaxation of ﬂuctuation for estimations of critical exponents
1. Size-Independent Scaling Analysis for Explosive Percolation
K. Hagiwara and Y. Ozeki Phys. Rev E 106, 054138 (2022).

PETERS, Robert [ C class; 2400 (B), 100 (C) ] (202)— Correlation induced phenomena in Kondo lattice systems
1. Quantum metric on the Brillouin zone in correlated electron systems and its relation to topology
for Chern insulators
T. Kashihara, Y. Michishita, and R. Peters Phys. Rev. B 107, 125116 (2023).
DOI:10.1103/PhysRevB.107.125116
2.

Low-energyexcitations and transport functions of the one-dimensional Kondo insulator
R. Peters and R. Rausch, submitted to SciPost Physics,

RAEBIGER, Hannes [ C class; 4000 (B), 350 (C) ] ()— First Principles calculation of carrier doping in Mott Insulators
SAITO, Shinji [ C class; 1200 (B), 0 (C) ] (283)— Theoretical stuidies on dynamics in biomolecular and liquid systems
SAKAGUCHI, Norihito [ C class; 7800 (B), 0 (C) ] (70)
— Reduction of Rare Metals in Fuel Cell and Formic Acid Decomposition Catalysts

416

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

SAKAI, Masatoshi [ B class; 300 (B), 70 (C) ] (338)— Charge distribution in low excitation state of organic charge order materials
SAKAI, Toru [ C class; 6600 (B), 700 (C) ] (223, 225)
— Magnetization Plateau with Spontaneous Symmetry Breaking— Numerical Study on Novel Spin Nematic Phase
1. Field-induced spin nematic Tomonaga-Luttinger liquid of the S = 1/2 spin ladder system withanisotropic ferromagnetic rung interaction
T. Sakai, R. Nakanishi, T. Yamada, R. Furuchi, H. Nakano, H. Kaneyasu, K. Okamoto and T.
Tonegawa, Phys. Rev. B 106 (2022) 064433
2.

S=2 Quantum Spin Chain with the Biquadratic Exchange Interaction
T. Sakai, T. Yamada, R. Nakanishi, R. Furuchi, H. Nakano, H. Kaneyasu, K. Okamoto and T.
Tonegawa, J. Phys. Soc. Jpn. 91 (2022) 074702

3.

The Haldane Gap of the S = 1 Heisenberg Antiferromagnetic Chain
H. Nakano, H. Tadano, N. Todoroki and T. Sakai, J. Phys. Soc. Jpn. 91 (2022) 074701

4.

Field-Induced Quantum Spin Nematic Liquid Phase in the S = 1 Antiferromagnetic Heisenberg
Chain with Additional Interactions
T. Sakai, H. Nakano, R. Furuchi and K. Okamoto, J. Phys.: Conf. Ser. 2164 (2022) 012030

5.

Magnetization plateau of the distorted diamond spin chain with anisotropic ferromagnetic interaction
T. Sakai, K. Okamoto, H. Nakano and R. Furuchi, AIP Adv. 12 (2022) 035030

6.

Spin Nematic Liquid of the S = 1/2 Distorted Diamond Spin Chain in Magnetic Field
T. Sakai, H. Nakano, R. Fruchi and K. Okamoto, AIP Adv. 13 (2023) 015313

7.

Quantum Phase Transition of the Shastry-Sutherland System and ESR Forbidden Transition
T. Sakai, R. Furuchi and H. Nakano, to appear in JPS Conf. Proc.

8.

Field-Induced Spin Nematic Liquid of the S = 1/2 Bond-Alternating Chain with the Anisotropy
R. Nakanishi, T. Yamada, R. Furuchi, H. Nakano, H. Kaneyasu, K. Okamoto, T. Tonegawa and
T. Sakai, to appear in JPS Conf. Proc.

9.

Translational Symmetry Broken Magnetization Plateau of the S = 2 Antiferromagnetic Chainwith Anisotropies
T. Yamada, R. Nakanishi, R. Furuchi, H. Nakano, H. Kaneyasu, K. Okamoto, T. Tonegawa and
T. Sakai, to appear in JPS Conf. Proc.

10. Numerical Study of S = 1/2 Heisenberg Antiferromagnet on the Floret Pentagonal Lttice
R. Furuchi, H. Nakano and T. Sakai, to appear in JPS Conf. Proc.
11. Large-Scale Numerical Diagonalization Study of the Shastry-Sutherland Model
H. Nakano and T. Sakai, to appear in JPS Conf. Proc.
12. Nematic Tomonaga-Luttinger Liquid Phase in an S = 1/2 Ferromagnetic-Antiferromagnetic BondAlternating Chain
T. Tonegawa, K. Okamoto, K. Nomura and T. Sakai, to appear in JPS Conf. Proc.
13. Quantum spin nematic liquid in the low-dimensional anisotropic magnets -S = 1/2 delta spin
chain with the anisotropic ferromagnetic interaction in magnetic ﬁeldT. Sakai, R. Furuchi, H. Nakano and K. Okamoto, to apper in SciPost Physics

417

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

SAKAKIBARA, Hirofumi [ B class; 500 (B), 0 (C) ] (172)— A study of nickelate superconductors in ﬁrst-principles
SAKASHITA, Tatsuya [ B class; 200 (B), 60 (C) ] (347)— Development of integrated interface of eigensolvers Rokko and application to quantum spin systems
SASAKI, Takehiko [ C class; 1200 (B), 200 (C) ] (127)— Study on oxidation and sulfurization of platinum nanoparticles
SATO, Masahiro [ D class; 6000 (B), 250 (C) ] (75)
— Discovery of Super-Composite Electrical Insulating Materials Based on Multiscale Physics and Deep
Learning of Polymers
1. Extrapolative molecular property prediction using quantum chemistry-assisted machine learning
for accelerated materials discovery
H. Shimakawa, A. Kumada, and M. Sato, Nature Communications, in preparation2.

Direct measurement of charge trap depth in polymer nanocomposites
H. Suzuki, Y. Kubo, Y. Sekiguchi, M. Kobayashi, A. Kumada, and M. Sato Journal of Physics D:
Applied Physics, under review

3.

Au/ポリプロピレン界面，Au/ポリイミド界面のバンドアラインメントの解明
D. Katase, H. Suzuki, R. Yoshinaga, M. Kobayashi, A. Kumada, and M. Sato IEEJ Transactions
on Fundamentals and Materials, accepted for publication

4.

Probing the Band Alignment at the Metal/Dielectric Polymer interface
D. Katase, H. Suzuki, M. Kobayashi, A. Kumada, and M. Sato Transactions on Dielectrics andElectrical Insulation, under review

SATO, Ryuhei [ C class; 1200 (B), 0 (C) ] (281)— the study on super ionic conduction and phase transition by disordering of complex hydride
1. Topological Data Analysis of Ion Migration Mechanism
R. Sato, K. Akagi, S. Takagi, K. Sau, K. Kisu, H. Li, and S.-i. Orimo, J. Chem. Phys. 158,
144116 (2023).
DOI:10.1063/5.0143387
2.

A Noise-Robust Data Assimilation Method for Crystal Structure Prediction Using Powder Diﬀraction Intensity
S. Yoshikawa, R. Sato, R. Akashi, S. Todo, and S. Tsuneyuki, J. Chem. Phys. 157, 224112(2022).
DOI:10.1063/5.0125553

SATO, Shunsuke [ C class; 3200 (B), 0 (C) ] (113)— First-principles analysis for nonlinear optical responses based on the transition current density
1. Frequency-resolved microscopic current density analysis of linear and nonlinear optical phenomena
in solids
S. A. Sato, arXiv.2302.05859
DOI:10.48550/arXiv.2302.05859

SAWABE, Kyoichi [ C class; 5200 (B), 0 (C) ] (85)
— Prediction of CO adsorption energy on single atom catalysts embedded in TiO2 by machine learning

418

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

SEKI, Yuya [ B class; 400 (B), 70 (C) ] (328)— Analysis of Ising model in statistical-mechanical informatics
1. Black-box optimization for integer-variable problems using Ising machines and factorization machines
Y. Seki, R. Tamura, and S. Tanaka, arXiv.2209.01016
DOI:10.48550/arXiv.2209.01016

SHAO, Cheng [ C class; 3200 (B), 350 (C) ] ()— Machine-learning optimization of ionic liquids for energy storage application
SHIMADA, Toshihiro [ B class; 400 (B), 80 (C) ] (174)— Computational search for narrow bandgap organic charge transfer complex
1. Molecular Dynamics Simulation of Poly(Ether Ether Ketone) (PEEK) Polymer to Analyze Intermolecular Ordering by Low Wavenumber Raman Spectroscopy and X-ray Diﬀraction
X. Yang, S. Yokokura, T. Nagahama, M. Yamaguchi, and T. Shimada, Polymers 14, 5406 (2022).
DOI:10.3390/polym14245406
2.

Synthesis of Epitaxial MoS2 /MoO2 CoreShell Nanowires by Two-Step Chemical Vapor Deposition
with Turbulent Flow and Their Physical Properties
M. Goto, I. Yamane, S. Arasawa, T. YAnase, S. Yokokura, T. Nagahama, Y. Chueh, Y. Shin, Y.
Kim, and T. Shimada, ACS Omega 7, 39362 (2022).
DOI:10.1021/acsomega.2c05586

SHIMAMURA, Kohei [ C class; 4200 (B), 350 (C) ] ()— Thermal Conductivity calculation with ANN potential for multi-component heterogeneous materials
1. Thermal conductivity calculation based on GreenKubo formula using ANN potential for β-Ag2 Se
Y. Takeshita, K. Shimamura, S. Fukushima, A. Koura, and F. Shimojo, J. Phys. Chem. Solids
163 (2022) 110580.
2.

Importance of Adjusting Coeﬃcients in Cost Function for Construction of High-Accuracy MachineLearning Interatomic Potential
A. Irie, K. Shimamura, A. Koura, and F. Shimojo, J. Phys. Soc. Jpn. 91 (2022) 045002.

SHIMOJO, Fuyuki [ C class; 5000 (B), 400 (C) ] (15)— First-Principles Molecular-Dynamics Study of Structural and Electronic Properties of Covalent Liquids and Glasses under Pressure
SHIMOKAWA, Tokuro [ C class; 4200 (B), 750 (C) ] (245)— Thermal eﬀects on quantum frustrated magnetisms— Thermal eﬀects on the frustrated magnets
1. Quantum paramagnetic states in the spin-1/2 distorted honeycomb-lattice Heisenberg antiferromagnet:Application to Cu2 (pymca)3 (ClO4 )T. Shimokawa, K. Takano, Z. Honda, A. Okutani, and M. Hagiwara, Phys. Rev. B 106, 134410(2022).
DOI:10.1103/PhysRevB.106.134410

SHINAOKA, Hiroshi [ C class; 4200 (B), 400 (C) ] (195)— First-principles calculations of dynamical susceptibilities for strongly correlated materials
SHINODA, Wataru [ E class; 29500 (B), 1300 (C) ] (216)— Large-scale Molecular Simulation of Soft Materials using All-Atom and Coarse-Grained Model

419

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

1.

Adsorption characteristics of peptides on ω-functionalized self-assembled monolayers: a molecular
dynamics study
H. O. S. Yadav, A.-T. Kuo, S. Urata, K. Funahashi, Y. Imamura, and W. Shinoda, Phys. Chem.
Chem. Phys. 24, 14805 (2022).
DOI:10.1039/D2CP01348G

2.

SPICA Force Field for Proteins and Peptides
S. Kawamoto, H. Liu, Y. Miyazaki, S. Seo, M. Dixit, R. DeVane, C. MacDarmaid, G. Fiorin, M.
L. Klein, and W. Shinoda, J. Chem. Theory Comput. 18, 3204 (2022).
DOI:10.1021/acs.jctc.1c01207

3.

Cooperative antimicrobial action of melittin on lipid membranes: A coarse-grained molecular
dynamics study
Y. Miyazaki and W. Shinoda, Biochim. Biophys. Acta-Biomembr. 1864, 183955 (2022).
DOI:10.1016/j.bbamem.2022.183955

4.

Amphotericin B assembles into seven-molecule ion channels: An NMR and molecular dynamics
study
Y. Umegawa, T. Yamamoto, M. Dixit, K. Funahashi, S. Seo, Y. Nakagawa, T. Suzuki, S. Matsuoka,
H. Tsuchikawa, Sh. Hanashima, T. Oishi, N. Matsumori, W. Shinoda, and M. Murata, Sci. Adv.
8, eabo2658 (2022).
DOI:10.1126/sciadv.abo2658

5.

Fluorescence Turn‐on of Tetraphenylethylene Derivative by Transfer from Cyclodextrin to Liposomes, HeLa cells, and E. coli
K. Masuda, R. Omokawa, R. Kawasaki, Y. Mise, Y. Ooyama, S. Harada, W. Shinoda, and A.
Ikeda, Chem. Eur. J. 29, e202203071 (2023).
DOI:10.1002/chem.202203071

6.

Liquid Structures and Ion Dynamics of Ionic Liquids viewed from Intermolecular Interactions
S. Tsuzuki, and W. Shinoda, Chem. Rec. e202200272 (2023).
DOI:10.1002/tcr.202200272

7.

Self-Assembly of Glycerol-Amphiphilic Janus Dendrimers Ampliﬁes and Indicates Principles for
the Selection of Stereochemistry by Biological Membranes
D. Zhang, Q. Xiao, M. Rahimzadeh, M. Liu, C. Rodriguez-Emmenegger, Y. Miyazaki, W. Shinoda,
and V. Percec, J. Am. Chem. Soc. 145, 4311 (2023).
DOI:10.1021/jacs.3c00389

SHINOHARA, Yasushi [ C class; 1800 (B), 0 (C) ] (124)— First-principles simulations for optical absorption of dielectrics under multi-color laser ﬁeld
1. Semiclassical description of electron dynamics in extended systems under intense laser ﬁelds
M. Tani, K. Sasaki, Y. Shinohara, and K. L. Ishikawa, Phys. Rev. B 106, 195141 (2022).
DOI:10.1103/PhysRevB.106.195141

SHIOMI, Junichiro [ C class; 9400 (B), 900 (C) ] (59)
— Development of Thermal Functional Materials Based on First-principles Simulations
1. Anharmonic phonon renormalization and thermal transport in the type-I Ba8 Ga16 Sn30 clathratefrom ﬁrst principles
M. Ohnishi, T. Tadano, S. Tsuneyuki, and J. Shiomi, Phys. Rev. B 106, 24303 (2022).
DOI:10.1103/PhysRevB.106.024303
2.

A Qualitative Study of the Disorder Eﬀect on the Phonon Transport in a Two-Dimensional

420

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Graphene/h-BN Heterostructure
Y Liu, W Ren, M An, L Dong, L Gao, X Shai, T Wei, L Nie, S Hu and C Zeng, Frontiers inMaterials 9, 913764 (2022).
DOI:10.3389/fmats.2022.913764
3.

Photonic design for color compatible radiative cooling accelerated by materials informatics
Jiang Guo, Shenghong Ju, Yaerim Lee, A. Alperen Gunay, Junichiro Shiomi, International Journalof Heat and Mass Transfer 195, 123193 (2022).
DOI:10.1016/j.ijheatmasstransfer.2022.123193

4.

P-TRANS: A Monte Carlo ray-tracing software to simulate phonon transport in arbitrary nanostructures
C. Shao, T. Hori, and J. Shiomi, Comput. Phys. Commun. 276, 108361 (2022).
DOI:10.1016/j.cpc.2022.108361

5.

Negligible contribution of inter-dot coherent modes to heat conduction in quantum-dot superlattice
C. Shao and J. Shiomi, Mater. Today Phys. 22, 100601 (2022).
DOI:10.1016/j.mtphys.2021.100601

SHIRAI, Tatsuhiko [ B class; 1000 (B), 80 (C) ] (290)— An Ising machine based on multi-spin ﬂip method— Quantum algorithm for an Ising machine
1. Multi-spin-ﬂip engineering in an Ising machine
T. Shirai and N. Togawa, IEEE Transactions on Computers 72 (2022) 759.
2.

Spin-Variable Reduction Method for Handling Linear Equality Constraints in Ising Machines
T. Shirai and N. Togawa IEEE Transactions on Computers (Early Access)

SHIRAISHI, Kenji [ C class; 10000 (B), 0 (C) ] (62,63)— First Principles Studies on Si-rich a-SiN for Flash Memory Application— First Principles Studies on Spin Tranfer Torque Magnetic Randum Access Memory (STT-MRAM)
1. Atomic and electronic structures of nitrogen vacancies in silicon nitride: Emergence of ﬂoating
gap states
F. Nanataki, K. Shiraishi, J. Iwata, Y. Matsushita, and A. Oshiyama, Phys. Rev. B, 106, (2022)
155201
2.

Eﬀect of interfacial nitrogen defects on tunnel magnetoresistance in an Fe/MgO/Fe magnetic
tunnel junction
Y. Ogawa, M. Araidai, T. Endoh, and K. Shiraishi, J. Appl. Phys., 132, (2022) 213904

3.

Microscopic physical origin of charge traps in 3D NAND ﬂash memories
F. Nanataki, J. Iwata, K. Chokawa, M. Araidai, A. Oshiyama, and K. Shiraishi, Jpn. J. Appl.
Phys., 62, (2023) SC1038

4.

Eﬀect of MgO Grain Boundaries on the Interfacial Perpendicular Magnetic Anisotropy in Spintransfer torque Magnetic Random-Access Memory: A First-Principles Study
K. Morishita, Y. Harashima, M. Araidai, T. Endoh, and K. Shiraishi, IEEE Transactions on
Magnetics, 59 (2023) 1

SHUDO, Ken-ichi [ C class; 1200 (B), 50 (C) ] (133)— Ultrafast adsorption states of two-dimensional metal-organic-frameworks and semiconductors
1. K-point longitudinal acoustic phonons are responsible for ultrafast intervalley scattering in mono-421

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

layer MoSe2
S. Bae, K. Matsumoto, H. Raebiger, K.-i. Shudo, Y.-H. Kim, Ø. S. Handegård, T. Nagao, M.
Kitajima, Y. Sakai, X. Zhang, R. Vajtai, P. Ajayan, J. Kono, J. Takeda, and I. Katayama, Nat.
Commun. 13, 4279 (2022).
DOI:10.1038/s41467-022-32008-6

SUGINO, Osamu [ E class; 16500 (B), 1300 (C) ] (47)
— Prediction of material properties using the density functional theory
1. Roadmap on Machine Learning in Electronic Structure
H. J. Kulik et al. Electron. Struct. 4, 023004 (2022).
DOI:10.1088/2516-1075/ac572f
2.

First-principles electronic structure investigation of HgBa2 Can − 1 Cun O2n+2+x with the SCAN
density functional
A. N. Tatan, J. Haruyama, and O. Sugino AIP Adv. 12, 105308 (2022).
DOI:10.1063/5.0098554

3.

Eﬀect of Nitrogen Doping and Oxygen Vacancy on the Oxygen Reduction Reaction on the Tetragonal Zirconia(101) Surface
S. Muhammady, J. Haruyama, S. Kasamatsu, and O. Sugino J. Phys. Chem. C 126, 15662 (2022).
DOI:10.1021/acs.jpcc.2c04132

SUWA, Hidemaro [ C class; 5000 (B), 450 (C) ] (240)— Large-scale simulation of spin dynamics in the vicinity of a phase transition point
1. Lifted directed-worm algorithm
H. Suwa, Phys. Rev. E 106, 055306 (2022).
DOI:10.1103/PhysRevE.106.055306
2.

Reducing rejection exponentially improves Markov chain Monte Carlo sampling
H. Suwa, submitted to Physica A: Statistical mechanics

SUZUKI, Takafumi [ C class; 2800 (B), 300 (C) ] (265)— Dynamical properties of the extended Kitaev-Γ model on a honeycomb lattice
SUZUKI, Takehito [ B class; 200 (B), 40 (C) ] (348)— Determining the period of the mainshock repetition with foreshocks in terms of permeability proﬁle— Estimation of the transition time from slow to fast earthquakes and its mathematical implications
1. Deriving the slip front propagation velocity with the slip-dependent and slip-velocity-dependent
friction laws via the use of the linear marginal stability hypothesis
T. Suzuki Phys. Rev. E 106, 015002 (2022).
DOI:10.1103/PhysRevE.106.015002

SUZUKI, Yoshikazu [ B class; 100 (B), 30 (C) ] ()— Electronic state calculation of inorganic energy materials using Quantum Espresso
SUZUKI, Yuji [ C class; 4400 (B), 0 (C) ] (95)
— Development of Polymer Electret Materials for Energy Harvesting Using Machine Learning
1. Property Prediction of Polymer Electret Material with Physics-informed Neural Network
Y. Zhang, K. Suzuki, and Y. Suzuki, 日本機械学会熱工学コンファレンス 2022, 東京, A234, (2022).
2.

Intrinsically-stretchable Polymer Electret for Powering Skin Electronics

422

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

R. Wang, K. Suzuki, T. Miyoshi, and Y. Suzuki, 1th Int. Conf. on Micro and Nanotechnology
for Power Generation and Energy Conversion Applications (PowerMEMS 2022), Salt Lake City,
T1A-01 (2022).
3.

エレクトレットを用いた環境発電
鈴木 雄二, 静電気学会誌, 49(2), 50 (2022).

TAKAYAMA, Akari [ B class; 300 (B), 0 (C) ] (344)— Structure analysis of borophene by using 2DMAT
1. Structure of χ3 borophene studied by total-reﬂection high-energy positron diﬀraction (TRHEPD)
Y. Tsujikawa, M. Shoji, M. Hamada, T. Takeda, I. Mochizuki, T. Hyodo, I. Matsuda, and A.
Takayama Molecules 27, 4219 (2022).
DOI:10.3390/molecules27134219

TAKETSUGU, Tetsuya [ C class; 2200 (B), 250 (C) ] (117)— Ab initio study on the structure and functions of nanomaterials
1. Superior Multielectron-Transferring Energy Storage by π-d Conjugated Frameworks
D. Xia, K. Sakaushi, A. Lyalin, K. Wada, S. Kumar, M. Amores, H. Maeda, S. Sasaki, T. Taketsugu, and H. Nishihara Small, 18, 2202861 (2022).
DOI:10.1002/smll.202202861

TAMURA, Ryo [ B class; 300 (B), 80 (C) ] (334)— Development of black-box optimization method for phase diagram
1. Machine-learning-based phase diagram construction for high-throughput batch experiments
R. Tamura, G. Deﬀrennes, K. Han, T. Abe, H. Morito, Y. Nakamura, M. Naito, R. Katsube,
Y. Nose, and K. Terayama Science and Technology of Advanced Materials: Methods 2 (2022)
153.
2.

Bayesian optimization package: PHYSBO
Y. Motoyama, R. Tamura, K. Yoshimi, K. Terayama, T. Ueno, and K. Tsuda, Comput. Phys.
Commun. 278 (2022) 108405.

3.

Automatic Rietveld reﬁnement by robotic process automation with RIETAN-FP
R. Tamura, M. Sumita, K. Terayama, K. Tsuda, F. Izumi, and Y. Matsushita Science and Technology of Advanced Materials: Methods 2 (2022) 435.

TANAKA, Shu [ B class; 300 (B), 70 (C) ] (336)— Study on novel algorithm for Ising machines
1. A phase-ﬁeld model by an Ising machine and its application to the phase-separation structure ofa diblock polymer
K. Endo, Y. Matsuda, S. Tanaka, and M. Muramatsu, Sci. Rep. 12, 10794 (2022).
DOI:10.1038/s41598-022-14735-4
2.

Black-box optimization for integer-variable problems using Ising machines and factorization machines
Y. Seki, R. Tamura, and S. Tanaka, submitted to IEEE Access.

3.

Towards optimization of photonic-crystal surface-emitting lasers via quantum annealing
T. Inoue, Y. Seki, S. Tanaka, N. Togawa, K. Ishizaki, and S. Noda Opt. Exp. 30, 43503 (2022).
DOI:10.1364/OE.476839

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

4.

Quantum annealing and computation: challenges and perspectives
B. K. Chakrabarti, H. Leschke, P. Ray, T. Shirai, and S. Tanaka Phil. Trans. Roy. Soc. A 381,
20210419 (2023).
DOI:10.1098/rsta.2021.0419

5.

ISAAQ: Ising Machine Assisted Quantum Compiler
S. Naito, Y. Hasegawa, Y. Matsuda, and S. Tanaka submitted to PRX Quantum.

TATETSU, Yasutomi [ C class; 3600 (B), 350 (C) ] (107)— Ab-initio research on nano structures of materials’ surfaces and grain boundaries with magnetic
elements
TEN-NO, Seiichiro L. [ D class; 10000 (B), 500 (C) ] (57)— Theoretical study of point defects in visible-light-driven semiconductor photocatalysts using ﬁrstprinciples calculations
TERAO, Takamichi [ B class; 1000 (B), 170 (C) ] (285)— Structural formation of inverse patchy particles
1. Eigenstates in gainloss systems of metallic-mean quasiperiodic chains
T. Terao, Physica D 444, 133612 (2023).

TEZUKA, Masaki [ C class; 3400 (B), 400 (C) ] (257)— Measurement-induced phase transition and chaotic dynamics in quantum many-body systems
1. Binary-coupling sparse Sachdev-Ye-Kitaev model: An improved model of quantum chaos andholography
M. Tezuka, O. Oktay, E. Rinaldi, M. Hanada, and F. Nori, Phys. Rev. B 107, L081103 (2023).
DOI:10.1103/PhysRevB.107.L081103
2.

Hayden-Preskill Recovery in Hamiltonian Systems
Y. Nakata and M. Tezuka, submitted to Phys. Rev. Lett.

Data Repository
Dataset for ”Binary-coupling sparse Sachdev-Ye-Kitaev model: an improved model of quantum chaosand holography”
https://zenodo.org/record/7514850
DOI:10.5281/zenodo.7514849

TODO, Synge [ C class; 6400 (B), 500 (C) ] (226)— Simulation of quantum many-body systems by tensor network and sampling
1. Optimized Implementation for Calculation and Fast-Update of Pfaﬃans Installed to the OpenSource Fermionic Variational Solver mVMC
R.Q. G. Xu, T. Okubo, S. Todo, and M. Imada Comp. Phys. Comm. 277, 108375 (2022).
DOI:10.1016/j.cpc.2022.108375
2.

Nematicity and fractional magnetization plateaus induced by spin-lattice coupling in the classicalkagome-lattice Heisenberg antiferromagnet
M. Gen and H. Suwa, Phys. Rev. B 105, 174424 (2022).
DOI:10.1103/PhysRevB.105.174424

3.

TeNeS: Tensor Network Solver for Quantum Lattice Systems
Y. Motoyama, T. Okubo, K. Yoshimi, S. Morita, T. Kato, and N. Kawashima, Comp. Phys.
Comm. 279, 108437 (2022).

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DOI:10.1016/j.cpc.2022.108437
4.

Quasi-Two-Dimensional Anomalous Hall Mott Insulator of Topologically Engineered Jeﬀ=1/2
Electrons
J. Yang, H. Suwa, D. Meyers, H. Zhang, L. Horak, Z. Wang, G. Fabbris, Y. Choi, J. Karapetrova,
J.-W. Kim, D. Haskel, P. J. Ryan, M. P. M. Dean, L. Hao, and J. Liu Phys. Rev. X 12, 031015(2022).
DOI:10.1103/PhysRevX.12.031015

5.

MateriApps LIVE! and MateriApps Installer: Environment for starting and scaling up materials
science simulations
Y. Motoyama, K. Yoshimi, T. Kato, and S. Todo, Software X 20, 101210 (2022).
DOI:10.1016/j.softx.2022.101210

6.

Bond-weighting method for the Grassmann tensor renormalization group
S. Akiyama, J. High Energy Phys. 11, 030 (2022).
DOI:10.1007/JHEP11(2022)030

7.

Lifted directed-worm algorithm
H. Suwa, Phys. Rev. E 106, 055306 (2022).
DOI:10.1103/PhysRevE.106.055306

8.

A Noise-Robust Data Assimilation Method for Crystal Structure Prediction Using Powder Diﬀraction Intensity
S. Yoshikawa, R. Sato, R. Akashi, S. Todo, and S. Tsuneyuki, J. Chem. Phys. 157, 224112(2022).
DOI:10.1063/5.0125553

TOHYAMA, Takami [ C class; 4200 (B), 0 (C) ] (198)— Transient absorption spectrum in photo-excited one-dimensional Mott insulator
1. Glassy dynamics of the one-dimensional Mott insulator excited by a strong terahertz pulse
K. Shinjo, S. Sota, and T. Tohyama, Phys. Rev. Res. 4, L032019 (2022).
DOI:10.1103/PhysRevResearch.4.L032019
2.

Controlling inversion and time-reversal symmetries by subcycle pulses in the one-dimensional
extended Hubbard model
K. Shinjo, S. Sota, S. Yunoki, and T. Tohyama, arXiv:2211.08694, submitted to Phys. Rev. B
DOI:10.48550/arXiv.2211.08694

TOKUMOTO, Yuki [ B class; 400 (B), 70 (C) ] (177)— Exploring dopants aiming at achieving high bulk insulation in Pb-based topological insulators
TONEGAWA, Takashi [ B class; 800 (B), 0 (C) ] (309)— Numerical Study of the One-Dimensional Quantum Spin Systems
1. S=2 Quantum Spin Chain with the Biquadratic Exchange Interaction
T. Sakai, T. Yamada, R. Nakanishi, R. Furuchi, H. Nakano, H. Kaneyasu, K. Okamoto, and T.
Tonegawa, J. Phys. Soc. Jpn. 91, 074702 (2022).
2.

Field-induced spin nematic Tomonaga-Luttinger liquid of the S=1/2 spin ladder system withanisotropic ferromagnetic rung interaction
T. Sakai, R. Nakanishi, T. Yamada, R. Furuchi, H. Nakano, H. Kaneyasu, K. Okamoto, and T.
Tonegawa, Phys. Rev. B. 106, 064433 (2022).

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TOYODA, Masayuki [ B class; 400 (B), 0 (C) ] (182)— Electronic Structure of Novel 2D Boron Monosulﬁde and Design of Related Materials
1. Rhombohedral Boron Monosulﬁde as a p-Type Semiconductor
N. Watanabe, K. Miyazaki, M. Toyoda, K. Takeyasu, N. Tsujii, H. Kusaka, A. Yamamoto, S. Saito,
M. Miyakawa, T. Taniguchi, T. Aizawa, T. Mori, M. Miyauchi, and T. Kondo, Molecules 28, 1896
(2023).
DOI:10.3390/molecules28041896
2.

Direct Imaging of Band Structure for Powdered Rhombohedral Boron Monosulﬁde by Microfocused ARPES
K. Sugawara, H. Kusaka, T. Kawakami, K. Yanagizawa, A. Honma, S. Souma, K. Nakayama,
M. Miyakawa, T. Taniguchi, M. Kitamura, K. Horiba, H, Kumigashira, T. Takahashi, S. Orimo,
M. Toyoda, S. Saito, T. Kondo, and T. Sato, Nano Lett. 23, 1673 (2023).
DOI:10.1021/acs.nanolett.2c04048

TRAN, Ba Hung [ E class; 2200 (B), 300 (C) ] (115)— Computational screening for magnetocaloric materials and permanent magnets
1. Direct and inverse magnetocaloric eﬀects in FeRh alloy
H. B. Tran, T. Fukushima, H. Momida, K. Sato, Y. Makino, and T. Oguchi J. Alloys Compd.
926, 166718 (2022).
DOI:10.1016/j.jallcom.2022.166718
2.

Eﬀect of magnetocrystalline anisotropy on magnetocaloric properties of AlFe2 B2 compound
H. B. Tran, H. Momida, Y. Matsushita, K. Sato, Y. Makino, K. Shirai, and T. Oguchi Phys. Rev.
B 105, 134402 (2022).
DOI:10.1103/PhysRevB.105.134402

3.

Insight into anisotropic magnetocaloric eﬀect of CrI3
H. B. Tran, H. Momida, Y. Matsushita, K. Shirai, and T. Oguchi Acta Mater. 231, 117851 (2022).
DOI:10.1016/j.actamat.2022.117851

4.

Dzyaloshinskii-Moriya interaction in Nd2 Fe14 B as the origin of spin reorientation and rotating
magnetocaloric eﬀect
H. B. Tran, and Y. Matsushita Appl. Mater. Today 32, 101825 (2023).
DOI:10.1016/j.apmt.2023.101825

5.

Skyrmions in van der Waals centrosymmetric materials with Dzyaloshinskii-Moriya interactions
H. B. Tran, and Y. Matsushita under review
DOI:10.48550/arXiv.2209.02333

TSUKAHARA, Noriyuki [ B class; 200 (B), 60 (C) ] (187)— DFT calculations of organic halogen molecules and their reaction products on metal surfaces
1. Substrate-Selective Intermolecular Interaction and the Molecular
1,3,5-Tris(4√ Self-Assemblies:√
bromophenyl)benzene Molecules on the Ag(111) and Si(111) ( 3 × 3)-Ag Surfaces
N. Tsukahara, and J. Yoshinobu, Langmuir 38, 8881 (2022).
DOI:10.1021/acs.langmuir.2c00991
2.

Surface-supported 2D metal-organic framework as a template to arrange metal clusters
N. Tsukahara, R. Arafune, and J. Yoshinobu, submitted to Langmuir426

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of TokyoTSUNEYUKI, Shinji [ C class; 6200 (B), 0 (C) ] (76)— First-principle study of photoresponsive functional materials
1. Lattice Dielectric Properties of Rutile TiO2 : First-Principles Anharmonic Self-Consistent Phonon
Study
T. Amano, T. Yamazaki, R. Akashi, T. Tadano, and S. Tsuneyuki, Phys. Rev. B 107, 094305(2023).
DOI:10.1103/PhysRevB.107.094305
2.

A noise-robust data assimilation method for crystal structure determination using powder diﬀraction intensity
S. Yoshikawa, R. Sato, R. Akashi, S. Todo and S. Tsuneyuki, J. Chem. Phys. 157, 224112(2022).
DOI:10.1063/5.0125553

3.

First-Principles Study of the Optical Dipole Trap for Two-Dimensional Excitons in Graphane
H. Katow, R. Akashi, Y. Miyamoto, and S. Tsuneyuki, Phys. Rev. Lett. 129, 047401 (2022).
DOI:110.1103/PhysRevLett.129.047401

4.

Anharmonic phonon renormalization and thermal transport in the type-I Ba8 Ga16 Sn30 clathratefrom ﬁrst principles
M. Ohnishi, T. Tadano, S. Tsuneyuki, and J. Shiomi, Phys. Rev. B 106, 024303 (2022).
DOI:10.1103/PhysRevB.106.024303

UCHIDA, Ken [ C class; 2800 (B), 400 (C) ] ()— Calculation of thermal and electrical properties of tungsten bronze
UCHIDA, Takashi [ B class; 200 (B), 60 (C) ] (346)— Magnetic structures of multiple-Q orders in inversion-symmetric Hubbard models
UJIHARA, Toru [ C class; 5200 (B), 0 (C) ] ()— Analysis of the behavior of third elements to the solvent in the SiC solution method
USUI, Hidetomo [ B class; 500 (B), 90 (C) ] (168)— First principles study on the band structures of high entropy compounds
WAKABAYASHI, Daisuke [ B class; 400 (B), 0 (C) ] (331)— Large-scale molecular-dynamics simulation of pressure-induced coordination change in silica glasswith ANN potentials
WATANABE, Hiroshi [ C class; 12200 (B), 950 (C) ] (218)— Molecular dynamics study of dynamic properties of interface structure— Non-equilibrium relaxation analysis on J1-J2 frustrated Ising model
1. Eﬀect of surfactants on the elasticity of the liquidliquid interface
S. Kikuchi and H. Watanabe, J. Chem. Phys. 158, 124901 (2023).
DOI:10.1063/5.0138733
2.

Non-monotonic behavior of the Binder parameter in discrete spin systems
H. Watanabe, Y. Motoyama, S. Morita, N. Kawashima, Prog. Theor. Exp. Phys. 2023, 033A02(2023).
DOI:10.1093/ptep/ptad022

WATANABE, Hiroshi [ B class; 400 (B), 80 (C) ] (207)— Uniﬁed description of cuprate high-temperature superconductors using multiband models

427

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

1.

Monte Carlo study of cuprate superconductors in a four-band d-p model: Role of orbital degrees
of freedom
H. Watanabe, T. Shirakawa, K. Seki, H. Sakakibara, T. Kotani, H. Ikeda, and S. Yunoki, J.
Phys.: Condens. Matter 35, 195601 (2023).
DOI:10.1088/1361-648X/acc0bf

WATANABE, Satoshi [ C class; 10800 (B), 900 (C) ] (56)
— Analyses on local properties at complex structures such as surfaces interfaces and defects via machinelearning potentials— Analyses related to atomic structures and atom dynamics at complex structures such as surfaces
interfaces and defects
1. Using neural network potentials to study defect formation and phonon properties of nitrogenvacancies with multiple charge states in GaN
K. Shimizu, Y. Dou, E.F. Arguelles, T. Moriya, E. Minamitani, and S. Watanabe, Phys. Rev. B
106, 054108 (2022).
DOI:10.1103/PhysRevB.106.054108
2.

Realistic simulation of thermoelectric characteristics of organic semiconductors based on electronic
structure calculations
M. Ohno, K. Shimizu, and S. Watanabe, Appl. Phys. Express 16, 011005 (2023).
DOI:10.35848/1882-0786/acaaee

YAMADA, Atsuo [ C class; 3200 (B), 350 (C) ] (111)— Theoretical Analysis on Structure/Electronic State of Novel Energy Storage Materials
1. Kinetic Square Scheme in Oxygen-Redox Battery Electrodes
K. Kawai, X. M. Shi, N. Takenaka, J. Jang, B. M. de Boisse, A. Tsuchimoto, D. Asakura, J.
Kikkawa, M. Nakayama, M. Okubo, and A. Yamada, Energy Environ. Sci. 15, 2591 (2022).
DOI:10.1039/D1EE03503G
2.

Anhydrous Fast Proton Transport Boosted by the Hydrogen Bond Network in a Dense Oxide-Ion
Array of α-MoO3
Z. Ma, X. M. Shi, S. Nishimura, S. Ko, M. Okubo, and A. Yamada, Adv. Mater. 34, 2203335(2022).
DOI:10.1002/adma.202203335

3.

Electrode Potential Inﬂuences the Reversibility of Lithium-Metal Anodes
S. Ko, T. Obukata, T. Shimada, N. Takenaka, M. Nakayama, A. Yamada, and Y. Yamada Nat.
Energy 7, 1217 (2022).
DOI:10.1038/s41560-022-01144-0

YAMADA, Atsushi [ C class; 800 (B), 200 (C) ] ()— Studies of the superconductivity and magnetic states in the strongly correlated electron systems using
Hubbard models.
YAMADA, Masahiko [ E class; 4000 (B), 850 (C) ] (246)— Density matrix renormalization group study of SU(N) Heisenberg models
1. Matrix Product Renormalization Group: Potential Universal Quantum Many-Body Solver
M. G. Yamada, T. Sanno, M. O. Takahashi, Y. Akagi, H. Suwa, S. Fujimoto, and M. Udagawa,submitted to Phys. Rev. Lett.

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YAMAGUCHI, Naoya [ B class; 700 (B), 100 (C) ] (156)— Development of Eﬃcient Evaluation Methods of Berry-phase-mediated Physical Properties for Large
Scale Systems Using the First-principles LCPAO Method
1. First-principles LCPAO approach for insulators under ﬁnite electric ﬁelds with forces
N. Yamaguchi and F. Ishii, Comput. Phys. Commun. 280, 108487 (2022).
DOI:10.1016/j.cpc.2022.108487
2.

First-principles calculation of anomalous Hall and Nernst conductivity by local Berry phase
H. Sawahata, N. Yamaguchi, S. Minami, and F. Ishii, Phys. Rev. B 107, 024404 (2023).
DOI:10.1103/PhysRevB.107.024404

3.

First-principles study of anomalous Hall eﬀect and anomalous Nernst eﬀect in Fe 2 Si
T. Tominaga, N. Yamaguchi, H. Sawahata, and F. Ishii, Jpn. J. Appl. Phys. 62, SD1019 (2023).
DOI:10.35848/1347-4065/acaca6

4.

Persistent spin helix on a diamond surface
H. P. Kadarisman, N. Yamaguchi, and F. Ishii, Appl. Phys. Express 16, 023001 (2023).
DOI:10.35848/1882-0786/acb486

5.

Seebeck-induced anomalous Nernst eﬀect in van der Waals MnBi2 Te4 layers
Y. Morishima, N. Yamaguchi, H. Sawahata, and F. Ishii, Appl. Phys. Express 16, 043003 (2023).
DOI:10.35848/1882-0786/accacc

6.

Prediction of wide-gap topological insulating phase in metastable BiTeI
Y. Zhang, N. Yamaguchi, H. Sawahata, and F. Ishii, Appl. Phys. Express, in press
DOI:10.35848/1882-0786/acccd3

YAMAJI, Youhei [ E class; 15000 (B), 1150 (C) ] (189)— Phase competition induced by long-range Coulomb interactions in candidate materials of Kitaev’squantum spin liquid
1. Superconductivity in Bilayer tt′ Hubbard Models
A. Iwano and Y. Yamaji, J. Phys. Soc. Jpn. 91, 094702 (2022).
DOI:10.7566/JPSJ.91.094702

YAMAMOTO, Tsuyoshi [ D class; 1000 (B), 0 (C) ] (297)— Theory of microwave scattering in a dissipative superconducting circuit element
YAMASHITA, Tomoki [ C class; 4000 (B), 350 (C) ] (103)— Development of crystal structure prediction method and material search
1. Hybrid algorithm of Bayesian optimization and evolutionary algorithm in crystal structure prediction
T. Yamashita, H. Kino, K. Tsuda, T. Miyake, and T. Oguchi, Sci. Technol. Adv. Mater. Meth
2, 67 (2022).
DOI:10.1080/27660400.2022.2055987
2.

Improvement of look ahead based on quadratic approximation for crystal structure prediction
T. Yamashita and H. Sekine, Sci. Technol. Adv. Mater. Meth 2, 84 (2022).
DOI:10.1080/27660400.2022.2059335

YAMAUCHI, Kunihiko [ C class; 3200 (B), 0 (C) ] (112)— First-principles design of novel topological materials and evaluation of anomalous Hall conductivity
1. Microscopic origin of magnetism in monolayer 3d transition metal dihalides429

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

K. Riedl, D. Amoroso, S. Backes, A. Razpopov, T. P. T. Nguyen, K. Yamauchi, P. Barone, S. M.
Winter, S. Picozzi, and R. Valenti Phys. Rev. B 106, 035156 (2022).
DOI:10.1103/PhysRevB.106.035156
2.

Rhombic Fermi surfaces in a ferromagnetic MnGa thin ﬁlm with perpendicular magnetic anisotropy
M. Kobayashi, N. H. D. Khang, T. Takeda, K. Araki, R. Okano, M. Suzuki, K. Kuroda, K. Yaji,
K. Sugawara, S. Souma, K. Nakayama, K. Yamauchi, M. Kitamura, K. Horiba, A. Fujimori, T.
Sato, S. Shin, M. Tanaka, and P. N. Hai, Phys. Rev. Mater. 6, 074403 (2022).
DOI:10.1103/PhysRevMaterials.6.074403

3.

Inﬂuence of Orbital Character on the Ground State Electronic Properties in the van Der Waals
Transition Metal Iodides VI3 and CrI3a) A. De Vita, T. T. P. Nguyen, R. Sant, G. M. Pierantozzi, D. Amoroso, C. Bigi, V. Polewczyk,
G. Vinai, L. T. Nguyen, T. Kong, J. Fujii, I. Vobornik, N. B. Brookes, G. Rossi, R. J. Cava, F.
Mazzola, K. Yamauchi, S. Picozzi, and G. Panaccione, Nano Lett. 22, 7034 (2022).
DOI:10.1021/acs.nanolett.2c01922

4.

Ab initio prediction of anomalous Hall eﬀect in antiferromagnetic CaCrO3
T. P. T. Nguyen and K. Yamauchi, Phys. Rev. B 107, 155126 (2023).
DOI:10.1103/PhysRevB.107.155126

YANAGISAWA, Susumu [ C class; 8000 (B), 0 (C) ] (68)
— First-principles band structure calculation with the electron-phonon coupling at ﬁnite temperature— First-principles bandstructure calculation of organic crystals with the electron-phonon coupling
1. Phonon dispersion of the organic semiconductor rubrene
K. Takada, K. Yoshimi, S. Tsutsui, K. Kimura, K. Hayashi, I. Hamada, S. Yanagisawa, N. Kasuya,
S. Watanabe, J. Takeya, and Y. Wakabayashi, Phys. Rev. B 105, 205205 (2022).
DOI:10.1103/PhysRevB.105.205205

YANAGISAWA, Takashi [ B class; 700 (B), 140 (C) ] (205)— Numerical study of electronic states in strongly correlated electron systems— Numerical study of electronic states of strongly correlated electron systems
1. Ferromagnetic diagonal stripe states in the two-dimensional Hubbard model with U ≤ ∞M. Miyazaki, T Yanagisawa Phys. Lett. A 446 (2022) 128276
2.

Quasi-ﬂat-band in s1/s2 pyrochlore oxides and the eﬀect of spin-orbit interaction
I. Hase, Y. Higashi, T. Yanagisawa J. Phys. Conf. Ser. 2164 (2022) 012063

3.

Field resilient superconductivity in atomic layer crystalline materials
Y. Higashi, S. Yoshizawa, T. Yanagisawa et al. unpublished

YASUDA, Chitoshi [ C class; 2400 (B), 0 (C) ] (271)— Numerical study of magnetism in the honeycomb-lattice spin systems
YASUDA, Yusuke [ B class; 900 (B), 160 (C) ] (295, 296)
— Rheology Simulation of Dynamically Cross-linked Elastomers— Structural Analysis and Mechanical Properties Simulations of Polymer Networks with Various Network Structures
1. Coarse-grained Molecular Dynamics Simulations of Dynamic Bond Elastomers using Inter-beadPotentials for Entropy- and Enthalpy-driven Mechanisms in Their Dynamics and Mechanical
Properties
Y. Yasuda, S. Nakagawa, H. Houjou, N. Yoshie and H. Morita, under review

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YOKO, Akira [ C class; 8600 (B), 800 (C) ] (64)
— Hydrogen production by water splitting on CeO2 (100) facet— Theoretical study for structure and property of metal oxide nanoclusters
YOKOI, Tatsuya [ C class; 4000 (B), 350 (C) ] (101)— High-accuracy machine-learning interactomic potential and massively large-scale molecular simulation
for miscroscopic mechanisms of grain-boundary-dislocation interactions
YOKOMORI, So [ B class; 500 (B), 90 (C) ] (166)— Study of eﬀects of modulation of molecular arrangement for electronic structure of single-component
molecular conductors with canted antiferromagnetism
YOSHIMOTO, Yuta [ C class; 8600 (B), 800 (C) ] (220)— Constructing a dataset of frequency-dependent dielectric properties of polymeric materials— Constructing a dataset of thermal and dielectric properties of ﬂuoropolymers
YUAN, Zhicheng [ C class; 200 (B), 100 (C) ] ()— Numerical study of the contact line dynamics on a hybrid hydrophilic surface431

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo◦ A classSince this class is for trial use, research reports are not required.
When other classes are also used, their publications are shown in the list of B–E classes.
Then, the pages of their reports and publications are given in ( ).
AKIYAMA, Ryota [ A class; 100 (B), 50 (C) ] ()— Band calculations of atomic-layer α-Sn on SnTeBUERKLE, Marius [ A class; 100 (B), 50 (C) ] (383)— First-principles computational study of nanocrystals for photovoltaic applications
FUKUI, Kiyu [ A class; 100 (B), 50 (C) ] ()— Program development of functional renormalization group for spin models with nondiagonal interactions
HASEGAWA, Taisuke [ A class; 100 (B), 50 (C) ] ()— Analyzing the pump-probe spectra by molecular dynamics simulations
HATANO, Naomichi [ A class; 100 (B), 50 (C) ] ()— Dynamics of Quantum Walks in Higher Dimensions
1. Proposal of multidimensional quantum walks to explore Dirac and Schrdinger systems
M. Yamagishi, N. Hatano, K.-I. Imura, and H. Obuse Phys. Rev. A 107, 042206 (2023).
DOI:10.1103/PhysRevA.107.042206

HOSONO, Nobuhiko [ A class; 100 (B), 50 (C) ] ()— Molecular Dynamics Simulation of Molecular Diﬀusion in Metal-Organic Frameworks
JESCHKE, Harald [ A class; 100 (B), 50 (C) ] (247, 248, 394)
— FLEX computation of susceptibility and superconductivity of β ′′ -(ET)2 X charge transfer salts
KAGAWA, Fumitaka [ A class; 100 (B), 50 (C) ] ()— Emergent Electric Field induced by current-driven ferromagnetic domain walls
KATO, Yasuyuki [ A class; 100 (B), 50 (C) ] ()— Theoretical study of topological phase transition and emergent electromagnetic phenomena in magnetic
metals
1. Hidden topological transitions in emergent magnetic monopole lattices
Y. Kato and Y. Motome, Phys. Rev. B. 107, 094437 (2023).
DOI:10.1103/PhysRevB.107.094437

KIM, Kang [ A class; 100 (B), 50 (C) ] ()— Molecular dynamics simulations for interfacial water in acrylate polymers
KOBAYASHI, Yoshihiro [ A class; 100 (B), 50 (C) ] ()— Molecular dynamics of nanocarbon heterostructure
MASAKI, Yusuke [ A class; 100 (B), 50 (C) ] ()— Non-axisymmetric vortices in topological nematic superﬂuids
MATSUMOTO, Ryosuke [ A class; 100 (B), 50 (C) ] ()— Pre-study for the dislocation-core structure analyses by DFT and atomistic calculations

432

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

NAKAZAWA, Kazuki [ A class; 100 (B), 50 (C) ] ()— Magnetization angle dependence of transport properties in ferromagnetic Weyl semimetal Co3 Sn2 S
NODA, Yusuke [ A class; 100 (B), 50 (C) ] ()— Microstructure analysis of polycrystalline silicon using ﬁrst-principles phase-ﬁeld method
OKITSU, Kouhei [ A class; 100 (B), 50 (C) ] ()— Calculation of X-ray diﬀfraction intensities from protein crystals based on the n-beam dynamical
diﬀraction therory
SATO, Taku [ A class; 100 (B), 50 (C) ] ()— Quantum pyrochlore magnet
TADA, Kohei [ A class; 100 (B), 50 (C) ] ()— Theoretical study for diradicals in solids and on surfaces
TAKAHASHI, Osamu [ A class; 100 (B), 50 (C) ] ()— Electronic structure of aqueous ionic liquids
TAKEMORI, Nayuta [ A class; 100 (B), 50 (C) ] ()— Band calculation of hypermaterials
TSUJI, Yuta [ A class; 100 (B), 50 (C) ] ()— Theoretical study of methane conversion catalysts by ﬁrst-principles calculations
UCHIDA, Kazuyuki [ A class; 100 (B), 50 (C) ] ()— First-principles Study on Superstructures of Si(111)-r7xr3-In Surface
UEHARA, Masatomo [ A class; 100 (B), 50 (C) ] ()— Throughput calculations of electron-lattice interactions
UEMURA, Naoki [ A class; 100 (B), 50 (C) ] ()— A benchmark test using OpenMX for alloy materials
YAMAMOTO, Go [ A class; 100 (B), 50 (C) ] ()— Machine learning-assisted high-throughput molecular dynamics simulation of high-mechanical performance CNT yarn structure
YAMAMOTO, Sayoko [ A class; 100 (B), 50 (C) ] ()— Polymer Design by Quantum Chemical Calculation
YANAGISHIMA, Taiki [ A class; 100 (B), 50 (C) ] ()— Origin and dynamics of devitriﬁcation in dense colloidal glasses
YUKAWA, Ryu [ A class; 100 (B), 50 (C) ] ()— Study of spin structure changes at Sb2 Te3 surfaces433

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

2 SCCMS Projects
FUJII, Mikiya [ 2000 (B), 200 (C) ] (368)— Data driven analysis for impurity eﬀects on photocatalysts
FUJITA, Takatoshi [ 3500 (B), 100 (C) ] (359)— Exciton Properties in Organic Optoelectronic Devices from Large-Scale Electronic Structure Calculations— Exciton Dynamics in Organic Optoelectronic Devices from Large-Scale Electronic Structure Calculations and Time-Resolved Spectroscopy
1. Ab Initio Study of Charge Separation Dynamics and PumpProbe Spectroscopy in the P3HT/PCBM
Blend
T. Fujita, T. Hoshi submitted
DOI:10.26434/chemrxiv-2023-773xs

FUKUSHIMA, Tetsuya [ 4000 (B), 400 (C) ] (364)— Development of fundamental simulation code for magnetic materials— Materials design of high performance magnetic materials
1. A ﬁrst-principles study on the electrical conductivity of Ag2 S1−x Sex (x = 0, 0.25, 0.5): Electronphonon coupling
H. N. Nam, K. Suzuki, A. Masago, T. Q. Nguyen, H. Shinya, T. Fukushima, and K. Sato Appl.
Phys. Lett. 120, 143903 (2022).
DOI:10.1063/5.0086703
2.

Direct and inverse magnetocaloric eﬀects in FeRh alloy: A theoretical study
H. B. Tran, T. Fukushima, H. Momida, K. Sato, and T. Oguchi, J. Alloys Compd. 926 166718(2022).
DOI:10.1016/j.jallcom.2022.166718

3.

Physics-informed machine learning combining experiment and simulation for the design of neodymiumiron-boron permanent magnets with reduced critical-elements content
A. Kovacs, J. Fischbacher, H. Oezelt, A. Kornell, Q. Ali, M. Gusenbauer, M. Yano, N Sakuma,
A. Kinoshita, T. Shoji, A. Kato, Y. Hong, S. Grenier, T. Devillers, N. Dempsey, T. Fukushima,
H. Akai, N. Kawashima, T. Miyake, and T. Schreﬂ, Front. Mater. 9, 1094055 (2023).
DOI:https://doi.org/10.3389/fmats.2022.1094055

4.

The role of electronphonon scattering on thermoelectric properties of intermetallic compounds
XSi (X = Co, Rh)
H. N. Nam, K. Suzuki, A. Masago, H. Shinya, T. Fukushima, K. Sato Jpn. J. Appl. Phys. 62,
020904 (2023).
DOI:10.35848/1347-4065/acb97a

GOHDA, Yoshihiro [ 2000 (B), 200 (C) ] (74)— Strain eﬀects at magnetic interfaces
1. Giant converse magnetoelectric eﬀect in a multiferroic heterostructure with polycrystalline Co 2 FeSi
S. Fujii, T. Usami, Y. Shiratsuchi, A.M. Kerrigan, A.M. Yatmeidhy, S. Yamada, T. Kanashima,
R. Nakatani, V.K. Lazarov, T. Oguchi, Y. Gohda, and K. Hamaya, NPG Asia Mater. 14, 43(2022).
DOI:10.1038/s41427-022-00389-1
2.

√√
Superconductivity in a two monolayer thick indium ﬁlm on Si(111) 3 × 3-B

434

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T. Ogino, I. Seo, H. Tajiri, M. Nakatake, S. Takakura, Y. Sato, Y. Hasegawa, Y. Gohda, K.
Nakatsuji, and H. Hirayama, Phys. Rev. B 106, 045423 (2022).
DOI:10.1103/PhysRevB.106.045423
3.

Origin of anisotropic magnetoresistance tunable with electric ﬁeld in Co 2 FeSi/BaTiO3 multiferroic
inetrfaces
S. Tsuna, R. Costa-Amaral, and Y. Gohda, J. Appl. Phys. 132, 234101 (2022).
DOI:10.1063/5.0128149

4.

First-principles phonon calculations of neodymium-magnet compounds
S. Tsuna and Y. Gohda, J. Appl. Phys. 133, 115103 (2023).
DOI:10.1063/5.0142945

5.

Structures of Sm-Cu intermetallics with Fe as subphase candidates in SmFe 12 -based permanent
magnets studied by ﬁrst-principles thermodynamics
S. Nishino and Y. Gohda, Jpn. J. Appl. Phys. 62, 030902 (2023).
DOI:10.35848/1347-4065/acc0b8

IMADA, Masatoshi [ 10000 (B), 1000 (C) ] (23)— Analysis on Superconducting Mechanism of high-Tc Superconductors— Systematic Analyses on Multi―Layer Copper Oxide Superconductors
1. Unconventional exciton evolution from the pseudogap to superconducting phases in cuprates
A. Singh, H. Y. Huang, J. D. Xie, J. Okamoto, C. T. Chen, T. Watanabe, A. Fujimori, M. Imada,and D. J. Huang, Nat. Commun. 13, 7906 (2022).
DOI:10.1038/s41467-022-35210-82.

Ab initiolow-energy eﬀective Hamiltonians for the high-temperature superconducting cuprates
Bi2 Sr2 CuO6 , Bi2 Sr2 CaCu2 O8 , HgBa2 CuO4 , and CaCuO2
Jean-Baptiste Morée, M. Hirayama, M. T. Schmid, Y. Yamaji, and M. Imada, Phys. Rev. B
106, 235150 (2022).
DOI:10.1103/PhysRevB.106.235150

3.

Unconventional dual 1D-2D quantum spin liquid revealed by ab initio studies on organic solids
familyK. Ido, K. Yoshimi, T. Misawa, and M. Imada, npj Quantum Mater. 7, 48 (2022).
DOI:0.1038/s41535-022-00452-8

4.

Optimized implementation for calculation and fast-update of Pfaﬃans installed to the open-source
fermionic variational solver mVMC
R.-Q. G. Xu, T. Okubo, S. Todo, and M. Imada, Compt. Phys. Commun., 277, 108375 (2022).
DOI:10.1016/j.cpc.2022.108375

MATUBAYASI, Nobuyuki [ 2000 (B), 500 (C) ] (360)— Development of an eﬃcient method for calculating miscibility of long-chain polymer blends using
molecular dynamics simulations
MIYAKE, Takashi [ 1800 (B), 70 (C) ] (366)— Development of high-performance permanent magnets by large-scale simulation and data-driven approach
1. A First-principles study on the stability of (R,Zr)(Fe,Co,Ti)12 against 2-17 and unary phases(R=Y, Nd, Sm)
T. Fukazawa, Y. Harashima, H. Akai, and T. Miyake, Phys. Rev. Mater. 6, 054404 (2022).
DOI:10.1103/PhysRevMaterials.6.054404

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2.

Explainable active learning in investigating structure-stability of SmFe 12−α−β Xα Yβ structures X,
Y= {Mo, Zn, Co, Cu, Ti, Al, Ga}
D.-N. Nguyen, H. Kino, T. Miyake, and H.-C. Dam MRS Bulletin 48, 1 (2023).
DOI:10.1557/s43577-022-00372-9

3.

Pareto front analysis and multi-objective Bayesian optimization for (R,Z)(Fe,Co,Ti)12 (R=Y, Nd,
Sm; Z=Zr, Dy)
T. Fukazawa and T. Miyake, J. Phys. Soc. Jpn. 92, 014801 (2023).
DOI:10.7566/JPSJ.92.014801

4.

Evidence-based data mining method to reveal similarities between materials based on physicalmechanisms
M.-Q. Ha, D.-N. Nguyen, H. Kino, Y. Ando, T. Miyake, T. Denœux, V.-N. Huynh, and H.-C.
Dam J. Appl. Phys. 133, 053904 (2023).
DOI:10.1063/5.0134999

OSHIYAMA, Atsushi [ 10000 (B), 1000 (C) ] (48)
— Quantum-theory-based multiscale simulation for next-generation power devices
1. An atomistic insight into reactions and free-energy proﬁles of NH3 and Ga on GaN surfaces during
the epitaxial growth
M. Boero, K. M. Bui, K. Shiraishi, K. Ishisone, Y. Kangawa, and A. Oshiyama, Appl. Surf. Sci.
599, 153935 (2022).
2.

Microscopic identiﬁcation of stepped SiC (0001) and the reaction site of hydrogen-rich epitaxial
growthT. Kimura, K. Chokawa, K. Shiraishi, and A. Oshiyama, Phys. Rev. B 106, 035309 (2022).

3.

Chickens or Eggs in the Atomic World: Structures and Electronic Properties of Defects in Semiconductors
A. Oshiyama JPSJ News and Comments 19, 12 (2022).

4.

Atomistic insight into initial stage of graphene formation on SiC (0001) surfaces
M. Boero, F. Imoto and A. Oshiyama, Phys. Rev. Materials 6, 093403 (2022).

5.

Atomic and electronic structures of nitrogen vacancies in silicon nitride: Emergence of ﬂoating
gap states
F. Nanataki, K. Shiraishi, J.-I. Iwata, Y.-i. Matsushita, and A. Oshiyama, Phys. Rev. B 106,155201 (2022).

6.

Insight into the Step Flow Growth of Gallium Nitride based on Density Functional Theory
K. M. Bui, K. Shiraishi and A. Oshiyama, Appl. Surf. Sci. 613, 155840 (2023).

7.

Atomic and electronic structures of interfaces between amorphous (Al2 O3 )1−x (SiO2 )x and GaN
polar surfaces revealed by ﬁrst-principles simulated annealing techniqueK. Chokawa, K. Shiraishi and A. Oshiyama, J. Appl. Phys 133, 065301 (2023).

8.

Microscopic physical origin of charge traps in 3D NAND ﬂash memories
F. Nanataki, J.-i. Iwata, K. Chokawa, M. Araidai, A. Oshiyama and K. Shiraishi, Jpn. J. Appl.
Phys. 62, SC1038 (2023).

SHIBA, Hayato [ 4000 (B), 400 (C) ] (353)— Graph neural network accelerated MD sampling436

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo— Development of surrogate model for liquid molecular dynamics based on graph neural networks
1. BOTAN: BOnd TArgeting Network for prediction of slow glassy dynamics by machine learning
relative motion
H. Shiba, M. Hanai, T. Suzumura, and T. Shimokawabe J. Chem. Phys. 158, 084503 (2023).
DOI:10.1016/j.cpc.2017.04.006

SUGINO, Osamu [ 2000 (B), 200 (C) ] (362)— Fugaku Battery & Fuel-Cell (B-1: Electrode interface reaction in fuel cell)
1. Eﬀect of Nitrogen Doping and Oxygen Vacancy on the Oxygen Reduction Reaction on the Tetragonal Zirconia (101) Surface
S. Muhammady, J. Haruyama, S. Kasamatsu, and O. Sugino, J. Phys. Chem. C126, 15662(2022).
DOI:10.1021/acs.jpcc.2c04132

TATEYAMA, Yoshitaka [ 5000 (B), 500 (C) ] (370)— Computational and Data Materials Science Study for ET Revolution by Developing Next-Generation
Battery and Fuel Cell
1. Atomistic insight into the dopant impacts at the garnet Li7 La3 Zr2 O12 solid electrolyte grainboundaries
B. Gao, R. Jalem, and Y. Tateyama J. Mater. Chem. A, 10, 10083 (2022).
DOI:10.1039/d2ta00545j
2.

Favorable Role of the MetalSupport Perimeter Region in Electrochemical NH 3 Synthesis: A Density Functional Theory Study on Ru/BaCeO3
A. Ishikawa, F. Murase, Y. Tateyama, and J. Otomo ACS Omega 7, 26107 (2022).
DOI:10.1021/acsomega.2c01222

3.

Tuning the Electronic, Ion Transport, and Stability Properties of Li-rich Manganese-based Oxide
Materials with Oxide Perovskite Coatings: A First-Principles Computational Study
Z. Zhou, D. Chu, B. Gao, T. Momma, Y. Tateyama, and C. Cazorla ACS Appl. Mater. Interfaces
14, 37009 (2022).
DOI:10.1021/acsami.2c07560

4.

High-Throughput Data-Driven Prediction of Stable High-Performance Na-Ion Sulﬁde Solid Electrolytes
S.-H. Jang, Y. Tateyama, and R. Jalem Adv. Funct. Mater. 32, 2206036 (2022).
DOI:10.1002/adfm.202206036

5.

Evaluation of battery positive-electrode performance with simultaneous ab-initio calculations ofboth electronic and ionic conductivities
H. D. Luong, C. Xu, R. Jalem, and Y. Tateyama J. Power Sources 569, 232969 (2023).
DOI:10.1016/j.jpowsour.2023.232969

6.

Nonequilibrium molecular dynamics for accelerated computation of ionion correlated conductivity
beyond NernstEinstein limitation
R. Sasaki, B. Gao, T. Hitosugi, and Y. Tateyama npj Comput. Mater. 9, 48 (2023).
DOI:10.1038/s41524-023-00996-8

TEN-NO, Seiichiro L. [ 2000 (B), 200 (C) ] (371)— Theoretical study of hydrogen-evolution semiconductor photocatalysts using ﬁrst-principles calculations
1. Characterization of Planar Defect in Layered Perovskite Photocatalyst Y 2 Ti2 O5 S2 by Electron

437

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Microscopy and First-Principles Calculations
M. Nakabayashi, K. Nishiguchi, X. Liang, T. Hisatomi, T. Takata, T. Tsuchimochi, N. Shibata,K. Domen, and S. L. Ten-no, J. Phys. Chem. C (in press).
DOI:10.1021/acs.jpcc.3c00820

YOSHIMI, Kazuyoshi [ 4000 (B), 400 (C) ] (355, 357)
— A systematic ab initio study of quasi-one-dimensional molecular conductors TM salts— Development of an eﬀective model estimation tool by Bayesian optimization
1. Comprehensive ab initio investigation of the phase diagram of quasi-one-dimensional molecularsolidsKazuyoshi Yoshimi, Takahiro Misawa, Takao Tsumuraya, and Hitoshi Seo, arXiv:2210.13726.
Data Repository
Ab initio Hamiltonians for molecular solids TMTTF and TMTSF salts
https://isspns-gitlab.issp.u-tokyo.ac.jp/k-yoshimi/tm-salts

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

2 Doctor Theses
1.

CHO, Sanghun
Theory of resonance-enhanced tunneling currents at semicondutor pn junctions
Chiba University, 2023-03

2.

HANA, Pratiwi KadarismanFirst-principles study of diamond surfaces
Kanazawa University, 2023-03

3.

HIDAKA, Yuichiro
Density Matrix Renormalization Group studies of quantum antiferromagnets on an anisotropic
triangular strip
The University of Tokyo, 2023-03

4.

INUI, Koji
Computational approaches in condensed matter physics using machine learning and automatic
diﬀerentiation
The University of Tokyo, 2022-09

5.

KOSHIRO, HidehikoTheoretical investigation of a frustrated spin ladder realizing multiple magnetization plateaus
The University of Tokyo, 2023-03

6.

MA, Zihan
Decoding fast proton intercalation mechanism for electrochemical energy storage devices
The University of Tokyo, 2022-09

7.

NARUTA, Hiroki
Constructing a high-pressure cell to go beyond 10 GPa using a multianvil press under high temperatures for in situ synchrotron x-ray measurements
Ehime University, 2023-03

8.

OGINO, Takuhiro
Phase diagrams and phase transitions of spin-1/2 XXZ ladder systems
The University of Tokyo, 2023-03

9.

OHNO, Masahiro
Exploration of organic thermoelectric materials via electronic structure calculations
The University of Tokyo, 2023-03

10. PHAM, Thanh Ngoc
First-principles investigations of NO chemistry on Cu surfaces and sintering-resistant properties
of Sr3 Ti2 O7 -supported Pd: towards the development of novel NOx puriﬁcation catalysts
Osaka University, 2022-09
11. TSUCHIMOTO, Akihisa
Mechanisms behind high-capacity generation in cathode materials for rechargeable batteries basedon earth-abundant elements
The University of Tokyo, 2023-03
12. WICAKSONO, Yusuf
Theoretical Study of Gap Opening/Closing Control of Dirac Cone Using Spin-dependent Potentials in Graphene-based Magnetic Junctions

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Osaka University, 2022-06
13. YAMANE, Ichiro
Synthesis and Function Exploration of New Composite Materials from Designable Precursors
Containing Carbon Using Ultrahigh Pressure
Hokkaido University, 2023-03
14. YANG, Xiaoran
Experimental and Simulation Studies on Crystal Growth and Crystallinity Evaluation of Organic
Materials and Polymers
Hokkaido University, 2023-03
15. YOSHII, Kiwamu
Studies on rheology of cohesive granular materials
Osaka University, 2023-03

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

2 Master Theses
1.

AKAMATSU, Katsuya
A Numerical Study on the Faithfulness of Real-Space Renormalization Group Maps
The University of Tokyo, 2022-9

2.

AOYAMA, Rina
Rational design of neutralizing antibodies against SARS-CoV-2 variants
The University of Tokyo, 2023-03

3.

ENOMOTO, Satoru
Evaluation of Thermodynamic Phase Stability of Sm-Fe-Cu Ternary Alloys by Cluster Expansion
Tokyo Institute of Technology, 2023-03

4.

FENG, Peijie
Eﬀect of Metal Nitride Formation on Ammonia Surface Reaction
The University of Tokyo, 2023-03

5.

FURUTA, Tatsuhiro
Hidden Markov simulation for time series data: Application to blinking phenomenon of quantum
dot
Kyushu Institute of Technology, 2021-03

6.

HASE, Tsubas
O vacancy induced local structural distortion and suppression of superconductivity in Sr 2 IrO4
Osaka University, 2023-03

7.

HAYAMA, Masaki
Friction Simulation of Semicrystalline Polymers by a Coarse-Grained Molecular Dynamics Method
University of Hyogo, 2023-03

8.

HENDRAWAN, JuhriFirst-Principles Study of Monolayer PbS on The Noble Metal Surfaces
Kanazawa University, 2023-03

9.

HIRAHARA, Yuushi
Correlation between band structure and molecular arrangement in organic molecular crystals
investigated by Van Der Waals Density Functional Theory and Wannier function
University of the Ryukyus, 2023-03

10. HIRAYAMA, Rumiko
Study of CO2 photoreduction process on nickel metal nanoparticle-supported ZrO2 catalyst surface
Chiba University, 2023-03
11. HOMMA, Kenji
Tensor Ring Decomposition Using Entanglement Branching Operator
The University of Tokyo, 2022-9
12. HORIE, Ryota
Estimation of the ground state energy of the triangular-lattice Heisenberg antiferromagnet byusing the spinon representation
Tokyo University of Science, 2023-03

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

13. ITO, Sho
Bayesian optimization of ﬂow synthesis processes in radical polymerization reactions
Nara Institute of Science and Technology, 2023-03
14. ITOMITSU, Yohei
Ab initio calculations for structural stability of transition-metal oxide superlattice
Kyushu Institute of Technology, 2022-03
15. IWAMURA, Kazuya
First-principles study of atomic and molecular adsorption on the Ag-In-Yb quasicrystal surface
Kagoshima University, 2023-03
16. JOO, Hankyul
Study of Electron Correlation Eﬀects on π-Electron-Proton Correlated Charge Ordering Transition in Cat-TTF-based Molecular Conductors
The University of Tokyo, 2023-03
17. KAMURA, Yugo
Numerical study on the control of gas-liquid interface geometry and pressure response
Ibaraki University, 2023-03
18. KITAHARA, Shinpei
Application of Machine Learning for Fast Evaluation of Chemisorption Energy Surface from theFirst Principles
Kagoshima University, 2023-03
19. MAKI, Yoshikazu
First-principles study on ferroelectricity of HfO2
Chiba University, 2023-03
20. MATSUBARA, Dai
Mesoscopic pattern formation of weakly-charged polyelectrolyte in binary solvent mixtures
Kyoto University, 2023-03
21. MISUMI, Shoji
Machine learning-assisted evaluation of phase transition temperature in antiferromagnetic quantum spin model with long-range interaction
University of Hyogo, 2022-03
22. MIZUHAR, Aoi
Study on initial interface formation in metal heteroepitaxial growth
Osaka City University, 2023-03
23. MURASE, Nobuaki
Microscopic origin of anomalous phase transition in self-propelled hard disk systems
Nagoya Institute of Technology, 2023-03
24. NIIMI, Kazuki
Eﬀect of metal substrates on the catalytic properties of light-element-doped graphene-supported
Pt single atoms
Hokkaido University, 2023-03
25. OGA, Satoshi
Sine-square deformation of Heisenberg model on polyhedra
The University of Tokyo, 2023-03

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

26. OHUCHI, Ryoga
First-Principles Theory of Photo-Excited Carriers Utilizing Maximally Localized Wannier Functions on GaAs Surface
Osaka University, 2023-03
27. OOISHI, Yasuhiro
Theoretical Study of Mechanical Characteristics of Linker Molecules Adsorbed on Graphene
Osaka University, 2023-03
28. OSHIMA, Hisanori
Measurement-induced criticality in a random quantum circuit with U (1) symmetry
The University of Tokyo, 2023-03
29. OTAKE, Yutaro
Inside deep neural network: insight from statistical physics
The University of Tokyo, 2023-03
30. SATO, Takumi
Crystal structure prediction for oxide lithium-ion conductors using ﬁrst-principles calculations
Nagaoka University of Technology, 2023-03
31. SEKINE, Hirotaka
Structure prediction of SrO on MgO substrate by ﬁrst-principles calculations
Nagaoka University of Technology, 2023-03
32. SHIBATA, Ukyo
First-principles study of β Ti-Al
Tokyo Institute of Technology, 2023-03
33. SHIMAMURA, Hirotaro
Rational design of a small protein targeting the immune checkpoint receptor PD-1
The University of Tokyo, 2023-03
34. SHINOHARA, Haru
Molecular dynamics simulation of polyrotaxane and slide-ring networks
The University of Tokyo, 2023-03
35. SOUNO, Kazuyoshi
Simple and eﬃcient method for local structure analysis in poly-disperse hard disk systems
Nagoya Institute of Technology, 2023-03
36. SUDO, Kosuke
First-Principles Studies on Atomic Structures of Oxidized Cu(111)Surfaces
Kagoshima University, 2023-03
37. SUZUKI, Haruto
Electrical Conduction Phenomena in Impurity-Doped Insulating Materials
The University of Tokyo, 2023-03
38. SUZUKI, Ken
Electronic structures and modulation spectroscopic properties of LiNbO2 thin ﬁlms
University of Fukui, 2023-03
39. TADOKORO, Arito

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Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Eﬀect of Inter-site Coulomb Interactions on Superconducting Gap Functions of BiS 2 -Based Layered Superconductors
Tokyo Metropolitan University, 2023-03
40. TAKEDA, Masashi
A series expansion study for magnon spectrum in Cs2Cu3SnF12 -estimations of exchange andDzyaloshinskii-Moriya interactionsTokyo University of Science, 2023-03
41. TAKEDA, Tomoya
Surface structure analysis of layered materials with Van der Waals bonds by total-reﬂection highenergy positron diﬀraction
Waseda University, 2023-03
42. TERANISHI, Mizuki
Theoretical design of a novel protein for suppression of allergic diseases induced by interleukin-33
The University of Tokyo, 2023-03
43. TERASHITA, Kodai
First-principles calculation for hydrogen diﬀusion and solubility in amorphous α-Al2O3
Hokkaido University, 2023-03
44. TEWENG, Yedija Yusua Sibuea
First-principles study of the perovskite-phase CsPbI3
Kanazawa University, 2023-03
45. TOMINAGA, TakahiroFirst-principles study of the magneto-thermoelectric eﬀect in Fe-Si alloys
Kanazawa University, 2023-03
46. TOMITA, Ami
Formation and electronic structures of nanodots in perovskite semiconductors; ﬁrst-principles
study
Chiba University, 2023-03
47. TSUNA, Shunsuke
First-principles phonon calculations of Nd-magnet compounds
Tokyo Institute of Technology, 2023-03
48. TSUNASHIMA, Mizuki
Study on Li ion diﬀusion in lithium lanthanum niobate based on ﬁrst-principles calculations
Tokyo University of Science, 2023-03
49. UEDA, Ryo
Machine Learning Molecular Dynamics Simulation of CO-driven Formation of Restructuring of
Stepped Cu Surfaces
Osaka University, 2023-03
50. URATA, Sora
Reduction of thermal conductivity at crystalline Si/amorphous SiO2 interfaces and its understanding via phonon transmission analysis
Osaka University, 2023-03
51. UZA, Ryo
Determination of charge injection levels in organic semiconductor ﬁlms by Van Der Waals Density444

Activity Report 2022 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Functional Theory and GW approximation
Graduate School of Engineering and Science, University of the Ryukyus, 2023-03
52. YAMAGUCHI, Takuma
Electronic structures and vibrational properties of YO monoxide thin ﬁlms
University of Fukui, 2023-03
53. ZHAO, Difei
First-principles study on the magnetic contribution to thermodynamics functions of Fe
Tokyo Institute of Technology, 2022-09
54. ZHAO, Wenxuan
Computational study for improving electrochemical stability of aqueous electrolytes
The University of Tokyo, 2023-03
55. ZHAO, Xun
Improved path-integral worm Monte Carlo method and its application to interacting bosons
The University of Tokyo, 2022-09445