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説明:

Efficient exploration of vast material spaces is a challenging task in materials science. Autonomous material search methods utilizing machine learning and ab initio calculations have emerged as powerful alternatives to traditional material discovery through synthesis and analysis, which is time-consuming and scope-limited. Although autonomous search methods have already been applied to various material spaces, they have not explored the extensive material space of Curie temperatures. Herein, we show a simulation-based autonomous search method that suggests ternary alloys with high Curie temperatures. The material space—consisting of disordered ternary magnetic alloys—is explored through Korringa–Kohn–Rostoker coherent potential approximation and Bayesian optimization. Over a continuous 10-day search, the system proposed several alloys—CoAuIr, CoPtPd, and CoFeBi—with Curie temperatures surpassing that of pure face-centered cubic Co. Although the insights gained through these predictions require further experi

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Machine learning, Autonomous, Curie temperature, ab initio

刊行年月日: 2024-12-31

出版者: Informa UK Limited

掲載誌:

• Science and Technology of Advanced Materials: Methods (ISSN: 27660400) vol. 4 issue. 1

研究助成金:

• Japan Science and Technology Corporation JPMJCR21O1

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1080/27660400.2024.2399494

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更新時刻: 2024-10-04 08:30:28 +0900

MDRでの公開時刻: 2024-10-04 08:30:28 +0900