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説明:

Exploring vast material spaces efficiently is challenging in materials science. Autonomous methods for material search—integrating machine learning and ab initio calculations—have emerged as powerful alternatives to traditional approaches, which are often time-consuming and limited in scope. Although these autonomous methods have been applied to various material systems, the extensive material space of B2 structured materials for half-metallicity remains largely unexplored. Herein, we introduce a simulation-based autonomous search approach to identify B2 structured alloys exhibiting high spin polarization of sp conduction electrons (Psp), sp minority spin band gap (Gsp), and Curie temperature (Tc). The proposed method explores the material space of disordered quaternary B2 magnetic alloys using the Korringa–Kohn–Rostoker coherent potential approximation and Bayesian optimization. Over a continuous search of approximately 100 days, the system identified Co1.0Mn0.7Al0.3 as a promising candidate, demonstrating high values of Psp, Gsp, and Tc. Although additional experimental and theoretical validation is necessary, this study demonstrates the potential of autonomous material search methods to expedite material discovery and enhance material property optimization.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Machine learning, Autonomous, ab initio, Half metal

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:

• Core Research for Evolutional Science and Technology JPMJCR21O1

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1080/27660400.2024.2403966

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更新時刻: 2024-10-04 08:30:32 +0900

MDRでの公開時刻: 2024-10-04 08:30:32 +0900