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説明:

Autonomous material search systems that combine ab initio calculations and Bayesian optimization are very promising for exploring huge material spaces. Setting up an appropriate material search space is necessary for efficient autonomous material search. However, performing the autonomous search within the material space set up using the prepared descriptors is not sufficient to obtain an efficient search, which can be achieved by prioritizing specific descriptors and properties. Here, a material search system that can autonomously search the huge material space while performing multi-objective optimization that considers similarities among elements and emphasizes specific descriptors is proposed. This system has been used for a material exploration of Heusler alloys. The system has successfully proposed several candidate materials with half-metallic properties. The proposed system is very versatile and can be applied to various properties and material systems.

権利情報:

キーワード: Machine learning, ab-initio , autonomous materials search

刊行年月日: 2022-12-31

出版者:

掲載誌:

• Science and Technology of Advanced Materials: Methods (ISSN: 27660400) vol. 2 issue. 1 p. 365-371

研究助成金:

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1080/27660400.2022.2123263

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更新時刻: 2024-01-05 22:14:05 +0900

MDRでの公開時刻: 2023-02-07 11:04:39 +0900