Machine learning potentials for multicomponent systems: The Ti-Al binary system

• 世古, 敦人

  Department of Materials Science and Engineering, Kyoto University

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.

収録刊行物

• Physical Review B

  Physical Review B 102 (17), 174104-, 2020-11-01

  American Physical Society

詳細情報

• CRID: 1050849378475291648
• NII論文ID: 120006898049
• ISSN: 24699950
• ISSN: 24699969
• DOI: 10.1103/physrevb.102.174104
• HANDLE: 2433/259295
• Web Site: https://link.aps.org/article/10.1103/PhysRevB.102.174104
• Web Site: http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevB.102.174104/fulltext
• 本文言語コード: en
• 資料種別: journal article
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