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First-principles study of rare-earth magnet compounds

The 1st R-CCS International Symposium “K and Post-K: Simuklation, BigData and AI supporting Society 5.0, Kobe International Conference Center, Kobe

2019年2月18日(月)

Takashi Miyake (AIST, NIMS)

Abstract

 Computational materials discovery is attracting interest recently. We report computational screening of rare-earth magnet compounds by first-principles calculation with the help of machine learning. The basic idea is to carry out high-throughput firstprinciples calculation of hypothetical compounds having various crystal structures and chemical compositions. To accelerate the screening, we construct a machine-learning model using kernel-ridge regression, which enables us to estimate materials properties efficiently. We use Orbital Field Matrix (OFM) [1] as a descriptor. In OFM, a crystal is divided by Voronoi polyhedra, and its local structure is expressed by a matrix using the information of electron configurations of constituent elements. Application to thousands of transition-metal compounds reveals that kernel-ridge regression with OFM reproduces the formation energy and local magnetic moments with high accuracy. Virtual screening of Nd-Fe-B systems using this technique will be presented. We also report subgroup relevance analysis of experimental Curie temperatures of rare-earth transition-metal bimetals [2].

[1] Tien-Lam Pham et al., Sci. Technol. Adv. Mater. 18, 756 (2017); J. Chem. Phys. 148,
204106 (2018).
[2] Hieu-Chi Dam et al., J. Phys. Soc. Jpn. 87, 113801 (2018).

その他特記事項

CDMSI, MI^2I



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