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ファイル | 記述 | サイズ | フォーマット | |
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PhysRevB.102.174104.pdf | 3.29 MB | Adobe PDF | 見る/開く |
タイトル: | Machine learning potentials for multicomponent systems: The Ti-Al binary system |
著者: | Seko, Atsuto https://orcid.org/0000-0002-2473-3837 (unconfirmed) |
著者名の別形: | 世古, 敦人 |
発行日: | 1-Nov-2020 |
出版者: | American Physical Society |
誌名: | Physical Review B |
巻: | 102 |
号: | 17 |
論文番号: | 174104 |
抄録: | Machine learning potentials (MLPs) are becoming powerful tools for performing accurate atomistic simulations and crystal structure optimizations. An approach to developing MLPs employs a systematic set of polynomial invariants including high-order ones to represent the neighboring atomic density. In this study, a formulation of the polynomial invariants is extended to the case of multicomponent systems. The extended formulation is more complex than the formulation for elemental systems. This study also shows its application to the Ti-Al binary system. As a result, an MLP with the lowest error and MLPs with high computational cost performance are selected from the many MLPs developed systematically. The predictive powers of the developed MLPs for many properties, such as the formation energy, elastic constants, thermodynamic properties, and mechanical properties, are examined. The MLPs exhibit high predictive power for the properties in a wide variety of ordered structures. The present scheme should be systematically applicable to other multicomponent systems. |
著作権等: | ©2020 American Physical Society 許諾条件に基づいて掲載しています。 |
URI: | http://hdl.handle.net/2433/259295 |
DOI(出版社版): | 10.1103/PhysRevB.102.174104 |
出現コレクション: | 学術雑誌掲載論文等 |
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