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In recent years, "Materials Informatics," which leverages data science for the exploration of novel materials, has gained significant attention. Among various approaches, "autonomous materials discovery," which integrates robotics and materials simulations with machine learning, has emerged as a promising method for efficiently navigating vast material spaces. In this study, we employed a simulation-based autonomous materials discovery approach that combines first-principles calculations with Bayesian optimization to explore a broad space of magnetic alloys. As a result, we successfully discovered and synthesized novel alloys with high magnetization.

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• In Copyright
  

  @公益社団法人 日本表面真空学会

Keyword: Machine learning, Materials Informatics

Date published: 2025-06-10

Publisher: 公益社団法人 日本表面真空学会

Journal:

• 表面と真空 (ISSN: 24335843) vol. 68 issue. 6 p. 328-332

Funding:

Manuscript type: Author's version (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.5998

First published URL: https://doi.org/10.1380/vss.68.328

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Updated at: 2025-12-12 08:30:25 +0900

Published on MDR: 2025-12-12 08:24:03 +0900