ISSP - The institute for Solid State Physics

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Division of Data-Integrated Materials Science, Social Cooperation Programs

Recently, application of the machine learning to materials science is an active research field, and many promising results have been reported. It is expected that machine learning may be a key to accelerating industrial application of basic science. This division aims at unifying the experiment and the numerical simulation through data scientific methodology, and thereby developing a framework for prediction and synthesis of innovative functional materials. Among many possible directions, we focus on permanent magnets and superconducting materials.

 

KAWASHIMA, Naoki (*) Research Contents Group's Homepage
(*) concurrent with Material Design and Characterization Laboratory