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説明:

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.

権利情報:

• In Copyright
  

  @公益社団法人 日本表面真空学会

キーワード: Machine learning, Materials Informatics

刊行年月日: 2025-06-10

出版者: 公益社団法人 日本表面真空学会

掲載誌:

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

研究助成金:

原稿種別: 著者最終稿 (Accepted manuscript)

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

公開URL: https://doi.org/10.1380/vss.68.328

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更新時刻: 2025-12-12 08:30:25 +0900

MDRでの公開時刻: 2025-12-12 08:24:03 +0900