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Seko, Atsuto (2023) Tutorial: Systematic development of polynomial machine learning potentials for elemental and alloy systems. Journal of Applied Physics, 133 (1) 11101 doi:10.1063/5.0129045

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Reference TypeJournal (article/letter/editorial)
TitleTutorial: Systematic development of polynomial machine learning potentials for elemental and alloy systems
JournalJournal of Applied Physics
AuthorsSeko, AtsutoAuthor
Year2023 (January 7)Volume133
Issue1
PublisherAIP Publishing
DOIdoi:10.1063/5.0129045Search in ResearchGate
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Mindat Ref. ID15662964Long-form Identifiermindat:1:5:15662964:7
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Full ReferenceSeko, Atsuto (2023) Tutorial: Systematic development of polynomial machine learning potentials for elemental and alloy systems. Journal of Applied Physics, 133 (1) 11101 doi:10.1063/5.0129045
Plain TextSeko, Atsuto (2023) Tutorial: Systematic development of polynomial machine learning potentials for elemental and alloy systems. Journal of Applied Physics, 133 (1) 11101 doi:10.1063/5.0129045
In(2023, January) Journal of Applied Physics Vol. 133 (1) AIP Publishing

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Togo, Atsushi, Seko, Atsuto (2024) On-the-fly training of polynomial machine learning potentials in computing lattice thermal conductivity. The Journal of Chemical Physics, 160 (21) doi:10.1063/5.0211296
Takahashi, Akira, Seko, Atsuto, Tanaka, Isao (2018) Linearized machine-learning interatomic potentials for non-magnetic elemental metals: Limitation of pairwise descriptors and trend of predictive power. The Journal of Chemical Physics, 148 (23). 234106pp. doi:10.1063/1.5027283
Naruse, Takuya; Seko, Atsuto; Tanaka, Isao (2025) A Systematic Approach to Crystal Structure Prediction Following Imaginary Phonon Modes Combined With Polynomial Machine Learning Potentials. Journal of the Physical Society of Japan, 94 (7). doi:10.7566/jpsj.94.074601
Seko, Atsuto (2020) Machine learning potentials for multicomponent systems: The Ti-Al binary system. Physical Review B, 102 (17) doi:10.1103/physrevb.102.174104
Wakai, Hayato, Seko, Atsuto, Tanaka, Isao (2023) Efficient global crystal structure prediction using polynomial machine learning potential in the binary Al–Cu alloy system. Journal of the Ceramic Society of Japan, 131 (10) 762-766 doi:10.2109/jcersj2.23053
Naruse, Takuya, Seko, Atsuto, Tanaka, Isao (2023) Global structure optimization following imaginary phonon modes accelerated by machine learning potentials in Cu, Ag, and Au. Journal of the Ceramic Society of Japan, 131 (10) 746-750 doi:10.2109/jcersj2.23056
Seko, Atsuto, Takahashi, Akira, Tanaka, Isao (2015) First-principles interatomic potentials for ten elemental metals via compressed sensing. Physical Review B, 92 (5) doi:10.1103/physrevb.92.054113
Seko, Atsuto, Maekawa, Tomoya, Tsuda, Koji, Tanaka, Isao (2014) Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids. Physical Review B, 89 (5) doi:10.1103/physrevb.89.054303
Seko, Atsuto, Hayashi, Hiroyuki, Nakayama, Keita, Takahashi, Akira, Tanaka, Isao (2017) Representation of compounds for machine-learning prediction of physical properties. Physical Review B, 95 (14) doi:10.1103/physrevb.95.144110
Vizoso, D., Dingreville, R. (2025) Decoding diffraction and spectroscopy data with machine learning: A tutorial. Journal of Applied Physics, 137 (13). doi:10.1063/5.0255593
Fujii, Susumu, Seko, Atsuto (2022) Structure and lattice thermal conductivity of grain boundaries in silicon by using machine learning potential and molecular dynamics. Computational Materials Science, 204. 111137pp. doi:10.1016/j.commatsci.2021.111137
 
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