ISSP - The institute for Solid State Physics

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Division of Data-Integrated Materials Science, Social Cooperation Research Department

Recently, machine learning has attracted social attention. The possibility of applying machine learning to material-science research is also actively studied, and many promising results have been reported. The expectation is that this idea will be the key to accelerating the industrial application of basic science. The division aims at developing methods for prediction of physical properties of materials, based on the understanding of electron correlation, by integrating experiments and numerical calculations through data-scientific approaches. While conventionally we have been comparing experimental results with numerical ones, interpreting the former by the latter, the new goal is to achieve something that cannot be done by experiment or numerical calculation alone, by using both of them simultaneously. In this way, we are searching for new materials that supports permanent magnetization, superconductivity, etc.


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