Activity Report 2020 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

3.6

Software Advancement Projectsand Workshop Support342Activity Report 2020 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

PHYSBO – optimization tools for PHYSics based on
Bayesian Optimization–
Ryo TAMURAInternational Center for Materials Nanoarchitectonics,
National Institute for Materials Science,
1-1 Namiki, Tsukuba, Ibaraki, 305-0044

Bayesian optimization (BO) is machinelearning based black-box optimization technique and has recently garnered signiﬁcant attention in physics, chemistry, and materials
science[1]. For example, for materials developments, the trial-and-error process to ﬁnd better material is regarded as optimizing a blackbox function where input is the composition,structure, and process and output will be materials property. In this algorithm, ﬁrst, aGaussian process regression that predicts theexpected property and variance is constructedfrom the already observed input-output pairs.
Next, through the trained Gaussian process,the probable input data that will yield the desired output value are also selected based on
the acquisition function using expected property and variance. Then, the true output value
for the selected candidate is obtained by experiments or simulations as black-box functions.
BO repeats these processes to ﬁnd better inputs. Although BO is powerful tool for blackbox optimization, BO is generally computationally expensive in two parts: training Gaussian process regression and optimizing acquisition function.
COMBO (COMmon Bayesian Optimization) has been developed mainly for researchers in the materials science ﬁeld[2]. In
the Gaussian process, two hyperparameters,i.e., parameters whose values were given prior
to learning, existed: the Gaussian kernel widthand noise variance. Using the COMBO pack-age, these hyperparameters were automaticallydetermined by maximizing the Type-II likelihood. In addition, to avoid computationallyexpensive for training Gaussian process regression, COMBO achieves high scalability using
Thompson sampling, random feature map, andone-rank Cholesky update,To accelerate COMBO package further,
PHYSBO (optimization tools for PHYSics
based on Bayesian Optimization) package isdeveloped as Python 3 code[3]. In PHYSBO,
the massive parallelization using ISSP supercomputer can be performed for optimizing acquisition function, and then both computationally expensive parts in BO can be resolved.
In addition, new function to perform multiobjective optimization is implemented.
In physics ﬁeld, so far, BO has been applied to some problems such as autonomous
X-ray scattering experiments [4], inverse scattering [5], crystal structure prediction [6], andeﬀective model estimation[7]. Thus, PHYSBO
package can accelerate these problems, andwill solve more complex physical problems using supercomputer.
This package is developed with Tsuyoshi
Ueno, Kei Terayama, Koji Tsuda, Yuichi
Motoyama, Kazuyoshi Yoshimi, and Naoki
Kawashima. We would like to thank the support from Project for advancement of software usability in materials science by The
Institute for Solid State Physics, The University of Tokyo, for development of PHYSBO.

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Activity Report 2020 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

References[1] K. Terayama, M. Sumita, R. Tamura, andK. Tsuda, Black-box optimization for automated discovery, Accounts of Chemical
Research 54, 1334 (2021).
[2] T. Ueno, T. D. Rhone, Z. Hou, T. Mizoguchi, and K. Tsuda, COMBO: An efﬁcient Bayesian optimization library formaterials science, Materials Discovery, 4,18 (2016).
[3] https://www.pasums.issp.u-tokyo.
ac.jp/physbo/[4] M. M. Noack, K. G. Yager, M. Fukuto,
G. S. Doerk, R. Li, and J. A. Sethian,
A kriging-based approach to autonomous
experimentation with applications to xray scattering, Scientiﬁc Reports 9, 1(2019).
[5] R. Vargas-Hernández, Y. Guan, D.
Zhang, and R. Krems, Bayesian optimization for the inverse scattering problem inquantum reaction dynamics, New Journalof Physics 21, 022001 (2019).
[6] T. Yamashita, N. Sato, H. Kino, T.
Miyake, K. Tsuda, and T. Oguchi, Crystal structure prediction accelerated bybayesian optimization, Physical Review
Materials 2, 013803 (2018).
[7] R. Tamura and K. Hukushima, Bayesian
optimization for computationally extensive probability distributions, PLoS ONE
13, e0193785 (2018).

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Activity Report 2020 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Development of MateriApps Installer
Synge TODO1,2 , Yuichi MOTOYAMA2 , Kazuyoshi YOSHIMI2 , and Takeo KATO2
1
Department of Physics, University of Tokyo, Tokyo, 113-0033
2Institute for Solid State Physics, University of Tokyo, Kashiwa, 277-8581

Nowadays, computer numerical computation is indispensable for theoretical researches
in materials science. For the advancement ofcomputational materials science, eﬃcient algorithms for solving equations of materials science are essential. Many excellent applications
based on state-of-the-art algorithms have beencreated so far. In 2013, we launched a portal site for materials science simulations, MateriApps [1], to disseminate information about
the developed software to experimentalists andcorporate researchers. We have been disseminating information about the application.
One of the obstacles for users to start using
published applications in materials science isinstalling software. MateriApps LIVE! [2] isan environment that allows users to quickly
try out computational materials science applications on their laptops and other devices.
MateriApps LIVE! is a Virtual Hard Disk Image (OVA) of VirtualBox that includes applications, OS (Debian GNU/Linux), editors, visualization tools, and other environments needed
to get started with the tutorial. Using MateriApps LIVE!, it is possible to quickly set up
a computing environment for participants inclasses and software training sessions.
However, the environment provided by MateriApps LIVE! is not enough to proceedwith full-scale simulations. Since MateriApps
LIVE! runs as a virtual machine, its computational power is somewhat limited. To support
users interested in larger-scale simulations, we
had started the development of MateriApps Installer in 2013.
As FY2020 Project for Advancement ofSoftware Usability in Materials Science (PASUMS), we have made several signiﬁcant updates on MateriApps Installer: i) organizeddirectory structure and scripts, ii) added fulldocumentation and tutorials, iii) upgradedsupported software, iv) supported new hardware [ISSP system B (ohtaka)], v) supported
new compilers [GCC 10 and Intel oneAPI].
Version 1.0 of MateriApps Installer was released in March 2021, which includes install
scripts for ALPS, ALPSCore, DSQSS, Quantum ESPRESSO, HΦ, Kω, LAMMPS, mVMC,OpenMX, RESPACK, and TeNeS. Also, it includes scripts for the following tools and libraries: Boost, CMake, Eigen3, FFTW, GCC,Git, GSL, HDF5, LAPACK, libﬃ, OpenBLAS,OpenMPI, OpenSSL, Python3, ScaLAPACK,Tcl/Tk, and zlib.
Using MateriApps Installer, the above material science applications have been preinstalled on the ISSP supercomputers (ohtakaand enaga). The source code of MateriApps
Installer is freely available from GitHub [4].
MateriApps Installer is distributed under
the GNU General Public License version 3(GPLv3). However, the patch ﬁles for eachsoftware are distributed under the license ofthe software.
[1] https://ma.issp.u-tokyo.ac.jp/[2] https://cmsi.github.io/
MateriAppsLive/[3] https://www.pasums.issp.u-tokyo.ac.
jp/mainstaller/[4] https://github.com/wistaria/MateriAppsInstaller/

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Activity Report 2020 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Advancement of the experimental data analysis for 2D
material structure
Takeo Hoshi
Department of Applied Mathematics and Physics, Tottori University,
4-101 Koyama-Minami, Tottori 680-8550, Japan.
20. April 2021 and was filled to capacity with

The open source software 2DMAT, a dataanalysis software of total-reflection high-30

energytokyo.ac.jp/event/4570).

positrondiffraction(TRHEPD)

experiment, was developed in Project forAdvancementof

Software

Usability

participants(https://ccms.issp.uSeveral

application

studies with 2DMAT are currently underway.

in

Our successor PASUMS project started inmaterials science (PASUMS) at FY2020 [1].

April 2021, so as to add several parallelized

TRHEPD is a novel experimental technique ofalgorithms and support other experimental

structure determination of two-dimensional

techniques of the structure determination ofmaterials and is being conducted intensively at

two-dimensional materials, such as surface X-the Slow Positron Facility of High Energy

ray diffraction experiment and low energy

Accelerator Research Organization (KEK) [2].

electron diffraction experiment.

We developed a python-based data analysis
software of TRHEPD at 2018 and 2019 [3].

References

The software uses an iterative optimization[1](Nelder-Mead) algorithm and the grid-basehttps://www.pasums.issp.utokyo.ac.jp/2DMAT

search algorithm. The software was used in[2] (Review) Y. Fukaya, et al, J. Phys. D: Appl.

several application studies [4, 5] in thePhys. 52, 013002 (2019).

collaboration[3] K. Tanaka, et al, Acta. Phys. Pol. A 137, 188

withexperimentalists,I.

Mochizuki (KEK), A. Takayama (Waseda U)(2020);and their co-workers.

https://arxiv.org/abs/2002.12165

In the PASUMS project at FY2020, weJJAPConf.

Series,inpress;[4] T. Hoshi, et al, Submitted; Preprint:

reorganized the software and added thehttps://arxiv.org/abs/2103.04875

Bayesian optimization algorithm, realized by[5] M. Hamada , et al, Poster presentation, JPS

the PHYSBO library [6],meeting, online, Mar. 2021.

and the replica-exchange Monte-Carlo algorithm. The software[6] T. Ueno, et al, Materials Discovery 4, 18

was called 2DMAT v.1 and was published at(2016);

Feb. 2021 [1]. A hands-on seminar was held attokyo.ac.jp/physbo346

https://www.pasums.issp.u-Activity Report 2020 / Supercomputer Center, Institute for Solid State Physics, The University of Tokyo

Report of CCMS hands-on sessions
in the 2020 ﬁscal year
Kota IDO
Institute for Solid State Physics, University of Tokyo
Kashiwa-no-ha, Kashiwa, Chiba 277-8581

In the 2020 ﬁscal year, Center for Computational Materials Science (CCMS)[1] in theInstitute for Solid State Physics (ISSP) held
one hands-on session using the ISSP supercomputer, which is shown in Table 1. In this report, we brieﬂy summarize the hands-on session in Table 1.
TeNeS is open-source software based on thetensor network method[3, 4]. Users can solve
two-dimensional quantum lattice models using the corner transfer matrix renormalizationgroup method for a projected entangled pairstate. Since TeNeS supports MPI/OpenMP
hybrid parallelization, this software eﬃciently
works on massively parallel machines such asenaga. Development of TeNeS was supportedby “Project for advancement of software usability in materials science” (PASUMS) [5] inthe 2019 ﬁscal year. In the hands-on session,
Tsuyoshi Okubo and co-developers explained
the basics of the tensor network method andgave a tutorial of TeNeS. Materials for thishands-on session are available on the oﬃcialsite (in Japanese) [6].

DateSoftware

Main lecturer

Nov. 10TeNeS

T. Okubo

References[1] https://ccms.issp.u-tokyo.ac.jp[2] https://ccms.issp.utokyo.ac.jp/event/3844[3] https://www.pasums.issp.utokyo.ac.jp/tenes/en[4] https://ma.issp.utokyo.ac.jp/en/app/2291[5] https://www.pasums.issp.u-tokyo.ac.jp/[6] https://www.pasums.issp.utokyo.ac.jp/tenes/doc/paper

Website[2]

Table 1: List of CCMS hands-on sessions using
ISSP supercomputer in the 2020 ﬁscal year.

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Activity Report 2020 / 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
1Institute for NanoScience Design, Osaka University,

1-2 Machikaneyama, Toyonaka, Osaka 560-0043, Japan
2Department of Precision Engineering, Graduate School of Engineering,Osaka University,

2-1 Yamada-oka, Suita, Osaka 565-0871, Japan
3Department of Electrical and Electronic Engineering,

Graduate School of Engineering, Kobe University,1-1 Rokkodaicho, Nada, Kobe 657-8501, JapanThe 37th Computational Materials Designbatteries and so on. The participants performed(CMD®) workshop (CMD37) has been held

the calculations and examined the results.

from August 31st to September 4th and the 38th

In CMD38 four participants took theCMD® workshop (CMD38) has been done fromsupercomputer

February 22nd to February 26th. Both were held

supercomputer of ISSP. They got a tutorial on

online. In this workshop we have theRSPACE developed by T. Ono and M. Uemoto.

supercomputer course to train up human

After giving the introductory lecture of large-resources to advance research by using system B

scale computing by M. Geshi and describing thesupercomputer of ISSP, the University of Tokyo.

calculation method of electronic states andIn CMD37 eight participants took theelectron conduction property using RSPACE,

supercomputer course and got a tutorial on

exercises published in the manual were carried

STATE-Senri developed by Y. Morikawa. Afterout. Then, electronic state calculations weregiving the introductory lecture of large-scale

carried out on a plurality of molecular systems,

computing by M. Geshi and explaining how toand the electronic density distribution was

use the supercomputer of ISSP and also how tovisualized.

use STATE-Senri, calculation models on each

optimization of graphene blisters, SiC/SiO2

research subject of the participants were built

interfaces, fullerenes, and ZnO was carried out.

and their calculations were carried out. Concrete

We would like to thank the organizers of thesubjects were molecular adsorption on solid

MateriApps workshop at the Institute for Solidsurfaces,

electrodeState Physics, the University of Tokyo, forinterfaces, adsorption and diffusion of atoms on

providing us with useful information for holding

graphene, electronic structures of oxides for ion

the workshop online.

chemical

reactionsat348

courseFinally,

theandatomic

used

thestructure