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Machine learning study of highly spin-polarized Heusler alloys at finite temperature

Ivan Kurniawan1,2, Yoshio Miura1,3,*, and Kazuhiro Hono1,2

  • 1Research Center for Magnetic and Spintronics Materials, National Institute for Materials Science (NIMS), 1-2-1 Sengen, Tsukuba 305-0047, Japan
  • 2Graduate School of Science and Technology, University of Tsukuba, Tsukuba 305-8577, Japan
  • 3Center for Spintronics Research Network (CSRN), Graduate School of Engineering Science, Osaka University, Machikaneyama 1-3, Toyonaka, Osaka 560-8531, Japan

  • *Corresponding author: miura.yoshio@nims.go.jp

Phys. Rev. Materials 6, L091402 – Published 14 September, 2022

DOI: https://doi.org/10.1103/PhysRevMaterials.6.L091402

Abstract

A huge magnetoresistance (MR) ratio exceeding 2000% at cryogenic temperature that was reported for half-metallic Heusler alloy based magnetic tunnel junctions showed large degradation at room temperature, which impedes practical application of Heusler alloy based MR devices. This motivates us to explore alternative Heusler alloys that show high spin polarization at finite temperatures. Here, we propose half-metallic Heusler alloys based on finite-temperature first-principles calculation via the disordered local moment method together with machine learning. We found several prospective materials at room temperature such as Co2MnGa0.2As0.8 and Co2FeAl0.4Sn0.6. We also investigated two combinatorial series, Co2MnGayAs1−y and Co2FeAlySn1−y, to understand the effect of alloy mixing on temperature dependence and found that Fermi level tuning significantly improved the spin polarization and its temperature dependence, especially in Co2FeAlySn1−y.

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