Elemental Reactivity Maps for Materials Discovery

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...

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Bibliographic Details
Main Author:	Yuki Inada (11624186) (author)
Other Authors:	Masaya Fujioka (2890817) (author), Haruhiko Morito (1963132) (author), Tohru Sugahara (1504459) (author), Hisanori Yamane (1420900) (author), Yukari Katsura (4949734) (author)
Published:	2025
Subjects:	Biochemistry Medicine Cancer Plant Biology Virology Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified used machine learning reported stable compounds machine learning models e ., synthesizability 81 </ sub 4 </ sub 26 </ sub 2 </ sub 19 </ sub predict elemental combinations identify elemental combinations elemental reactivity maps novel inorganic materials positive data sets negative data sets data sets reactive combinations high reactivity materials discovery materials databases unreported combinations search efficiency greatly improve essential issue easily collected dimensional heatmaps constituent elements
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Elemental Reactivity Maps for Materials Discovery

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