The 9th International Conference on Multiscale Materials Modeling

Presentation information

Symposium

D. Data-Driven and Physics-Informed Materials Discovery and Design

[SY-D5] Symposium D-5

Fri. Nov 2, 2018 9:45 AM - 11:00 AM Room8

Chair: Jörg Neugebauer(MPIE, Germany)

[SY-D5] Computational exploration of strong permanent magnet compounds

Invited

Takashi Miyake1,2,3 (1.CD-FMat, AIST, Japan, 2.ESICMM, NIMS, Japan, 3.CMI2, NIMS, Japan)

Modern strong magnets are rare-earth magnets in which high saturation magnetization and high Curie temperature come from transition-metal 3d electrons, and strong magnetorcystalline anisotropy originates from rare-earth 4f electrons [1]. There are various types of crystal structures and chemical composition, and exploration of a new magnet compound is a hot topic. Among them, RFe12-type compounds with the ThMn12 structure are attracting renewed interest because of their high iron content. Recently synthesized NdFe12Nx film has higher saturation magnetization and anisotropy field than Nd2Fe14B, although its bulk phase is thermodynamically unstable. I will present a first-principles study on the effect of element substitution on magnetism and structural stability. I will also discuss how machine learning accelerates magnetic-materials discovery. Application to the Curie temperature of RFe12-type compounds shows that Bayesian optimization offers an efficient way to optimize chemical composition of magnet compounds. Kernel ridge regression using orbital-field matrix as a descriptor reproduces the magnetic moment and formation energy of thousands of transition-metal compounds in reasonable accuracy [2], which can be utilized for virtual screening of new magnetic compounds. Bayesian optimization approach to crystal structure prediction is also presented [3].
[1] Takashi Miyake and Hisazumi Akai, J. Phys. Soc. Jpn. 87, 041009 (2018).
[2] T.L. Pham et al., Sci. Tech. Adv. Mater. 18, 756 (2017).
[3] T. Yamashita et al., Phys. Rev. Mater. 2, 013803 (2018).