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Machine learning in computational magnetic materials discovery

日本物理学会2018年秋季大会

2018年9月10日(月)

Takashi Miyake

Abstract

Computational design of a new compound is a big challenge in condensed-matter physics overyears. High-throughput computational screening is recently attracting interest as a possibletool for solving this issue. The basic idea is the following. (1) We first collect structure data ofexisting compounds from database. (2) Hypothetical compounds are generated by elementsubstitution. (3) Physical properties of these compounds are evaluated by first-principlescalculation. If good properties are obtained, the compound is recommended as a candidatecompound. A bottleneck of this method is that the step (3) is computationally demanding. Inthis talk, I will discuss how machine learning can be utilized to accelerate the computationalscreening by taking transition-metal compounds as an example. We propose orbital field matrix(OFM) [1] as a descriptor to represent a compound (Fig.1). Application to thousands oftransition-metal compounds reveals that the kernel ridge regression using OFM reproducesboth the formation energy and magnetic moments in reasonable accuracy. Virtual screening ofNd-Fe-B systems using this technique will be presented. We also discuss how machinelearning can be used to extract controlling parameters of magnetic properties. We analyzeexperimental Curie temperature of 108 rare-earth transition-metal bimetals using kernel ridgeregression. Full-search analysis reveals that the rare-earth concentration is the most relevantparameter (good descriptor) [2].
[1] T.L. Pham et al., STAM 18, 756 (2017); JCP 148, 204106 (2018).
[2] H.C. Dam et al., arXiv:1705.00978.


研究活動

文部科学省

文部科学省
元素戦略プロジェクト(活動紹介)
NIMS磁石パートナーシップ

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