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The concept of material informatics is becoming more advanced and prospective in research on magnetic materials. This can be evidenced by several successful recent studies utilizing different tools of machine learning and demonstrating new opportunities in the field of permanent magnets, magnetocaloric materials, and magnetic recording in hard disk drives. This trend is also promoted by the intensive accumulation of scientific data, growth of computational performance and the progress with algorithms. This report presents our recent progress on implementing machine learning into two different studies: the development of rare-earth free Fe2P-type magnetocaloric compounds for cryogenic applications, and high-throughput characterization of FePt granular media for heat-assisted magnetic recording.

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Keyword: Nd-Fe-B magnets, Magnetocaloric materials, Magnetic recodring, Machine learning

Conference: The 238th Topical Symposium of the Magnetics Society of Japan (2022-10-25)

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Manuscript type: Not a journal article

MDR DOI: https://doi.org/10.48505/nims.4852

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Updated at: 2025-04-10 21:41:57 +0900

Published on MDR: 2024-10-16 16:30:21 +0900