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

Ivan Kurniawan SAMURAI ORCID (National Institute for Materials ScienceROR) ; Yoshio Miura SAMURAI ORCID (National Institute for Materials ScienceROR) ; Kazuhiro Hono SAMURAI ORCID (National Institute for Materials ScienceROR)

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Ivan Kurniawan, Yoshio Miura, Kazuhiro Hono. Machine learning study of highly spin-polarized Heusler alloys at finite temperature. Physical Review Materials. 2022, (), L091402.
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(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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Keyword: spintronics, half-metal, Heusler alloy, machine learning, disordered local moment, Bayesian optimization

Date published: 2022-09-14

Publisher: American Physical Society (APS)

Journal:

  • Physical Review Materials (ISSN: 24759953) L091402

Funding:

  • JSPS and JST-CREST 17H06152, 20H02190, 22H04966, JPMJCR21O1

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1103/physrevmaterials.6.l091402

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Updated at: 2024-01-05 22:14:00 +0900

Published on MDR: 2023-07-10 13:30:32 +0900

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