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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

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: Machine learning, Autonomous, Curie temperature, ab initio

Date published: 2024-12-31

Publisher: Informa UK Limited

Journal:

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

Funding:

• Japan Science and Technology Corporation JPMJCR21O1

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

MDR DOI:

First published URL: https://doi.org/10.1080/27660400.2024.2399494

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Updated at: 2024-10-04 08:30:28 +0900

Published on MDR: 2024-10-04 08:30:28 +0900