Yuichi Motoyama

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Takahiro MisawaInstitute for Solid State Physics (ISSP), University of TokyoVerified email at issp.u-tokyo.ac.jp
Mitsuaki KawamuraInformation Technology center, The University of TokyoVerified email at ds.itc.u-tokyo.ac.jp
Takeo KatoInstitute for Sold State Physics, The University of TokyoVerified email at issp.u-tokyo.ac.jp
Kota IdoUniversity of TokyoVerified email at issp.u-tokyo.ac.jp
Naoki KawashimaInstitute for Solid State Physics, University of TokyoVerified email at issp.u-tokyo.ac.jp
Satoshi MoritaGraduate School of Science and Technology, Keio UniversityVerified email at keio.jp
Synge TodoUniversity of TokyoVerified email at phys.s.u-tokyo.ac.jp
Junya OtsukiOkayama UniversityVerified email at cmpt.phys.tohoku.ac.jp
Takeo HoshiTottori UniversityVerified email at damp.tottori-u.ac.jp
Terumasa TadanoNational Institute for Materials ScienceVerified email at nims.go.jp
Yusuke NomuraInstitute for Materials Research, Tohoku UniversityVerified email at tohoku.ac.jp
Kazuma NakamuraDepartment of Basic Sciences, Kyushu Institute of TechnologyVerified email at mns.kyutech.ac.jp
Youhei YamajiQMM Group, MANA, National Institute for Materials ScienceVerified email at nims.go.jp
Taichi KosugiQuemix Inc. and University of TokyoVerified email at m.titech.ac.jp
Kei TerayamaYokohama City UniversityVerified email at yokohama-cu.ac.jp
Koji TsudaProfessor, GSFS, The University of TokyoVerified email at k.u-tokyo.ac.jp
Ryo TamuraNational Institute for Materials ScienceVerified email at nims.go.jp
Hiroshi ShinaokaAssociate Professor, Saitama UniversityVerified email at mail.saitama-u.ac.jp
Shusuke KasamatsuYamagata UniversityVerified email at sci.kj.yamagata-u.ac.jp
Masayuki OhzekiGraduate School of Information Sciences, Tohoku UniversityVerified email at tohoku.ac.jp

Yuichi Motoyama

The Institute for Solid State Physics, The University of Tokyo

Verified email at issp.u-tokyo.ac.jp

Computational Physics Statistical Physics


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mVMC—Open-source software for many-variable variational Monte Carlo method T Misawa, S Morita, K Yoshimi, M Kawamura, Y Motoyama, K Ido, ... Computer Physics Communications 235, 447-462, 2019	101	2019
RESPACK: An ab initio tool for derivation of effective low-energy model of material K Nakamura, Y Yoshimoto, Y Nomura, T Tadano, M Kawamura, T Kosugi, ... Computer Physics Communications 261, 107781, 2021	63	2021
Bayesian optimization package: PHYSBO Y Motoyama, R Tamura, K Yoshimi, K Terayama, T Ueno, K Tsuda Computer Physics Communications 278, 108405, 2022	40	2022
SpM: Sparse modeling tool for analytic continuation of imaginary-time Green’s function K Yoshimi, J Otsuki, Y Motoyama, M Ohzeki, H Shinaoka Computer Physics Communications 244, 319-323, 2019	15	2019
Path-integral Monte Carlo method for the local Z 2 Berry phase Y Motoyama, S Todo Physical Review E 87 (2), 021301, 2013	15	2013
Robust analytic continuation combining the advantages of the sparse modeling approach and the Padé approximation Y Motoyama, K Yoshimi, J Otsuki Physical Review B 105 (3), 035139, 2022	13	2022
Asymmetric melting of a one-third plateau in kagome quantum antiferromagnets T Misawa, Y Motoyama, Y Yamaji Physical Review B 102 (9), 094419, 2020	9	2020
TeNeS: Tensor network solver for quantum lattice systems Y Motoyama, T Okubo, K Yoshimi, S Morita, T Kato, N Kawashima Computer Physics Communications 279, 108437, 2022	8	2022
Z_N Berry phase and symmetry protected topological phases of SU (N) antiferromagnetic Heisenberg chain Y Motoyama, S Todo Physical Review B 98, 195127, 2015	8	2015
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, T Hoshi Computer Physics Communications 280, 108465, 2022	7	2022
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, T Hoshi Computer Physics Communications 277, 108371, 2022	7	2022
Facilitating ab initio configurational sampling of multicomponent solids using an on-lattice neural network model and active learning S Kasamatsu, Y Motoyama, K Yoshimi, U Matsumoto, A Kuwabara, ... The Journal of Chemical Physics 157 (10), 104114, 2022	6	2022
DSQSS: Discrete Space Quantum Systems Solver Y Motoyama, K Yoshimi, A Masaki-Kato, T Kato, N Kawashima Computer Physics Communications 264, 107944, 2021	6	2021
Kω—Open-source library for the shifted Krylov subspace method of the form (zI− H) x= b T Hoshi, M Kawamura, K Yoshimi, Y Motoyama, T Misawa, Y Yamaji, ... Computer Physics Communications 258, 107536, 2021	5	2021
Data Analysis of Ab initio Effective Hamiltonians in Iron-Based Superconductors—Construction of Predictors for Superconducting Critical Temperature K Ido, Y Motoyama, K Yoshimi, T Misawa Journal of the Physical Society of Japan 92 (6), 064702, 2023	3	2023
Non-monotonic behavior of the Binder parameter in discrete spin systems H Watanabe, Y Motoyama, S Morita, N Kawashima Progress of Theoretical and Experimental Physics 2023 (3), 033A02, 2023	3	2023
Dimer-Mott and charge-ordered insulating states in the quasi-one-dimensional organic conductors - and R Kobayashi, K Hashimoto, N Yoneyama, K Yoshimi, Y Motoyama, ... Physical Review B 96 (11), 115112, 2017	3	2017
Universal and Non-Universal Correction Terms of Bose Gases in Dilute Region: A Quantum Monte Carlo Study A Masaki-Kato, Y Motoyama, N Kawashima Journal of the Physical Society of Japan 91 (2), 024001, 2022	2	2022
Enabling ab initio configurational sampling of multicomponent solids with long-range interactions using neural network potentials and active learning S Kasamatsu, Y Motoyama, K Yoshimi, U Matsumoto, A Kuwabara, ... arXiv preprint arXiv:2008.02572, 2020	2*	2020
Update of HΦ: Newly added functions and methods in versions 2 and 3 K Ido, M Kawamura, Y Motoyama, K Yoshimi, Y Yamaji, S Todo, ... Computer Physics Communications 298, 109093, 2024	1	2024

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