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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.

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• 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.

Keyword: Crystal structure, Elements, Machine learning, Materials, Reactivity

Date published: 2025-03-25

Publisher: American Chemical Society (ACS)

Journal:

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

Funding:

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

Manuscript type: Author's version (Accepted manuscript)

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

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

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Updated at: 2026-02-21 16:30:08 +0900

Published on MDR: 2026-02-21 13:38:36 +0900