[22a-B201-1~11] 10.1 Emerging materials in spintronics and magnetics (including fabrication and characterization methodologies)

▲ [22a-B201-7] Machine Learning Study of Highly Spin-Polarized Heusler Alloys at Finite Temperature

〇(D)Ivan Kurniawan^1,2, Yoshio Miura^1,3, Kazuhiro Hono^1,2 (1.NIMS, 2.Univ. of Tsukuba, 3.Osaka Univ.)

Keywords:First-principles, Finite temperature, Machine learning

Strong reduction of magnetoresistance (MR) ratio in Heusler alloys based MR devices at finite temperature implies the importance of exploration of new highly spin-polarized Heusler alloys. However, recent high throughput calculation and machine learning combined with first principles to find prospective Heusler alloys only performed with 0 K assumption, which lead to the significant discrepancy of material prediction with experiments. In this work, we carried out the finite temperature first-principles calculation combined with machine learning to find new Heusler alloys. We employed Bayesian optimization for the machine learning algorithm and the disordered local moment method for finite temperature effect, respectively. We successfully found several new prospective Heusler alloys with high spin polarization such as Co₂MnGa_0.2As_0.8 and Co₂FeAl_0.4Sn_0.6. Furthermore, the effect of alloy mixing on the temperature dependence of Co₂MnGa_yAs_1-y and Co₂FeAl_ySn_1-y is also discussed.