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説明:

When searching for novel inorganic materials, limiting the combination of constituent elements can greatly improve the search efficiency. In this study, we used machine learning to predict elemental combinations with high reactivity for materials discovery. The essential issue for such prediction is the uncertainty of whether the unreported combinations are nonreactive or not just investigated, though the reactive combinations can be easily collected as positive data sets from the materials databases. To construct the negative data sets, we developed a process to select reliable nonreactive combinations by evaluating the similarity between unreported and reactive combinations. The machine learning models were trained by both data sets, and the prediction results were visualized by two-dimensional heatmaps: elemental reactivity maps to identify elemental combinations with high reactivity but no reported stable compounds. The maps predicted high reactivity (i.e., synthesizability) for the Co–Al–Ge ternary system, and two novel ternary compounds were synthesized: Co4Ge3.19Al0.81 and Co2Al1.26Ge1.74.

権利情報:

• In Copyright
  This document is the Accepted Manuscript version of a Published Work that appeared in final form in Chemistry of Materials, copyright © 2025 The Authors. Published by American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.chemmater.4c02259.

キーワード: Crystal structure, Elements, Machine learning, Materials, Reactivity

刊行年月日: 2025-03-25

出版者: American Chemical Society (ACS)

掲載誌:

• Chemistry of Materials (ISSN: 08974756) vol. 37 issue. 6 p. 2097-2105

研究助成金:

• Core Research for Evolutional Science and Technology JPMJCR19J1
• Japan Science and Technology Agency JPMJFS2108

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.5510

公開URL: https://doi.org/10.1021/acs.chemmater.4c02259

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更新時刻: 2026-02-21 16:30:08 +0900

MDRでの公開時刻: 2026-02-21 13:38:36 +0900