We change the experimental searches for new inorganic materials more efficient and innovative, with the power of data science. From mathematics and machine learning, we develop an original simulator like a 3D puzzle, which assists us to design periodic crystal structures. Large-scale experimental searches for new compounds are carried out by Na-flux method and proton-driven ion implantation. Out of the newly-investigated compounds, we select the candidate functional materials such as thermoelectric materials, by first-principles calculations and machine learning of literature data. We will then design the best electrodes for the material, to fabricate an application device made of the new material.
(国研)物質・材料研究機構 統合型材料開発・情報基盤部門 材料データプラットフォームセンター 主任研究員 Senior Researcher, Materials Data Platform Center, Materials Database Group, Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Japan.
東京大学 大学院 新領域創成科学研究科 物質系専攻 助教 Assistant Professor, Department of Advanced Materials Science, Graduate School of Frontier Sciences, The University of Tokyo