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Machine-learning assisted approaches for large scale simulations

Created 01 Aug 2023| Updated 20 Nov 2023 | 5 articles

Edited by

Satoshi Watanabe(University of Tokyo)

Tomofumi Tada(Kyushu University)

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Review Article
Configuration sampling in multi-component multi-sublattice systems enabled by ab Initio Configuration Sampling Toolkit (abICS)
Shusuke Kasamatsu, Yuichi Motoyama, Kazuyoshi Yoshimi & Tatsumi Aoyama

Originally published in Science and Technology of Advanced Materials: Methods, Volume: 3, Number: 1 (31 Dec 2023)

Published online: 15 Dec 2023
AbstractforConfiguration sampling in multi-component multi-sublattice systems enabled by ab Initio Configuration Sampling Toolkit (abICS) | Full Text | References | PDF (6.4 MB) | EPUB
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Review Article
Applications and training sets of machine learning potentials
Changho Hong, Jaehoon Kim, Jaesun Kim, Jisu Jung, Suyeon Ju, Jeong Min Choi & Seungwu Han

Originally published in Science and Technology of Advanced Materials: Methods, Volume: 3, Number: 1 (31 Dec 2023)

Published online: 30 Oct 2023
AbstractforApplications and training sets of machine learning potentials | Full Text | References | PDF (7.8 MB) | EPUB
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Article
Fully autonomous materials screening methodology combining first-principles calculations, machine learning and high-performance computing system
Akira Takahashi, Kei Terayama, Yu Kumagai, Ryo Tamura & Fumiyasu Oba

Originally published in Science and Technology of Advanced Materials: Methods, Volume: 3, Number: 1 (31 Dec 2023)

Published online: 04 Oct 2023
AbstractforFully autonomous materials screening methodology combining first-principles calculations, machine learning and high-performance computing system | Full Text | References | PDF (3.8 MB) | EPUB
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Article
Prediction of Born effective charges using neural network to study ion migration under electric fields: applications to crystalline and amorphous Li3PO4
Koji Shimizu, Ryuji Otsuka, Masahiro Hara, Emi Minamitani & Satoshi Watanabe

Originally published in Science and Technology of Advanced Materials: Methods, Volume: 3, Number: 1 (31 Dec 2023)

Published online: 04 Sep 2023
AbstractforPrediction of Born effective charges using neural network to study ion migration under electric fields: applications to crystalline and amorphous Li3PO4 | Full Text | References | PDF (8.5 MB) | EPUB | Supplemental
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Article
Strategy for building training set for neural-network potentials for ionic diffusion in solids: example for hydride-ion diffusion in LaHO
Albert Iskandarov

Originally published in Science and Technology of Advanced Materials: Methods, Volume: 3, Number: 1 (31 Dec 2023)

Published online: 28 Jul 2023
AbstractforStrategy for building training set for neural-network potentials for ionic diffusion in solids: example for hydride-ion diffusion in LaHO | Full Text | References | PDF (6.6 MB) | EPUB | Supplemental
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