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

3.6

Software Advancement Projectsand 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 anfor

project 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.

onfirst-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 usinglearning 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:

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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.

theWe 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.

which4.

allowsforchangesin

A subset of samples from step 3 arerelaxed and their total energies areReferences

calculated

using[1] S. Kasamatsu and O. Sugino, J. Phys.:calculation.

Ifthefirst-principles
resultsdeviate

Condens. Matter 31, 085901 (2019).

considerably 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 forCIT/aenet-lammps

the first-principles calculations and neural[6] https://github.com/toml-lang/toml

network training/evaluation are controlled byFig. 1: Overview of the active learning procedure using abICS.

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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 Yoshimib
aDepartment 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 ata 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 theTOML 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 Number21H01041.

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, andY. Suzumura, J. Phys. Soc. Jpn. 73, 3135
(2004).
[9] K. Yoshimi, M. Nakamura, and H. Mori, J.
Phys. Soc. Jpn. 76, 024706 (2007).

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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 forFeatures of each software are as follows.

Computational Materials Science (CCMS) in the2DMAT 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

wheretheISSP

supercomputer was used [1-8]. In this report, weinitio

softwareframework

toperform

briefly summarize them.

configuration samplings in disordered systems

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 methodsscience” (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 athe ISSP supercomputer was used in the 2022

software package of the quantum Monte Carlofiscal year.

method based on Feynman’s path integral

DateApr. 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.

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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

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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 theM. 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.

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materials

sandwiched

used

theanddialectic

betweenmetallic