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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.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: Machine learning, Autonomous, ab initio, Half metal

Date published: 2024-12-31

Publisher: Informa UK Limited

Journal:

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

Funding:

• Core Research for Evolutional Science and Technology JPMJCR21O1

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

MDR DOI:

First published URL: https://doi.org/10.1080/27660400.2024.2403966

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Updated at: 2024-10-04 08:30:32 +0900

Published on MDR: 2024-10-04 08:30:32 +0900