ホーム > 研究活動 > 2016年 > Novel mixture model for the representation of potential energy surfaces

研究活動

Novel mixture model for the representation of potential energy surfaces

Journal of Chemical Physics 145, 154103 (2016)

2016年10月17日(月)

Tien Lam Pham, Hiori Kino, Kiyoyuki Terakura, Takashi Miyake, and Hieu Chi Dam ( doi.org/10.1063/1.4964318 )

Abstract

We demonstrate that knowledge of chemical physics on a materials system can be automatically extracted from first-principles calculations using a data mining technique; this information can then be utilized to construct a simple empirical atomic potential model. By using unsupervised learning of the generative Gaussian mixture model, physically meaningful patterns of atomic local chemical environments can be detected automatically. Based on the obtained information regarding these atomic patterns, we propose a chemical-structure-dependent linear mixture model for estimating the atomic potential energy. Our experiments show that the proposed mixture model significantly improves the accuracy of the prediction of the potential energy surface for complex systems that possess a large diversity in their local structures.

その他特記事項

さきがけ, MI^2I


研究活動

元素戦略拠点

触媒・電池元素戦略拠点
触媒・電池元素戦略研究拠点 (京都大学)
東工大元素戦略拠点
東工大元素戦略拠点 (東京工業大学)
構造材料元素戦略研究拠点
構造材料元素戦略研究拠点 (京都大学)
高効率モーター用磁性材料技術研究組合
高効率モーター用 磁性材料技術研究組合