2023 Fiscal Year Final Research Report
Computational design of functional core using informatics approaches
| Project Area | New Materials Science on Nanoscale Structures and Functions of Crystal Defect Cores |
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| Project/Area Number |
19H05787
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| Research Category |
Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
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| Allocation Type | Single-year Grants |
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| Review Section |
Science and Engineering
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| Research Institution | The University of Tokyo |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
世古 敦人 京都大学, 工学研究科, 准教授 (10452319)
豊浦 和明 京都大学, 工学研究科, 准教授 (60590172)
柴田 基洋 東京大学, 生産技術研究所, 助教 (40780151)
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| Project Period (FY) |
2019-06-28 – 2024-03-31
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| Keywords | 機械学習 / 機能コア / 格子欠陥 / 界面 / 転位 / 第一原理計算 / 計測インフォマティクス / マテリアルズインフォマティクス |
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| Outline of Final Research Achievements |
We have achieved significant results in the development and analysis of methods utilizing machine learning. First, we improved computational and analytical efficiency by tens of thousands of times using machine learning potentials, contributing to the exploration of crystal structures and the study of silicon thermal function cores in collaboration with other teams. Additionally, we elucidated the structure-function correlation of lattice defects and developed efficient property prediction methods. Furthermore, we established high-precision analysis methods for ion functional cores, enabling data-driven exploration of new materials using functional cores in collaboration with the other teams. Moreover, we advanced the field of measurement informatics in cooperation with the other teams. The codes and databases developed by us have been widely released, providing valuable resources to the research community and promoting further advancements in functional-core materials science.
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| Free Research Field |
材料科学
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は，機械学習ポテンシャルを用いた材料開発の効率化により，迅速な材料発見と環境負荷の低減という社会的課題の解決に寄与している．計測インフォマティクス手法の開発を通じて，精度の高いデータ解析が可能となり，材料分析が加速された．公開されたデータベースやコードは多くの研究者に利用され，イノベーションを促進することが期待される．さらに，機械学習ポテンシャルの開発により，格子欠陥の構造機能相関を明らかにし，高精度なイオン機能コア解析手法やデータ駆動型の新材料探索手法が確立された．これにより，学術界の研究手法の高度化と新しい機能コア学理の構築に貢献した．
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