| Budget Amount *help |
¥130,000,000 (Direct Cost: ¥100,000,000、Indirect Cost: ¥30,000,000)
Fiscal Year 2023: ¥21,450,000 (Direct Cost: ¥16,500,000、Indirect Cost: ¥4,950,000)
Fiscal Year 2022: ¥24,700,000 (Direct Cost: ¥19,000,000、Indirect Cost: ¥5,700,000)
Fiscal Year 2021: ¥26,000,000 (Direct Cost: ¥20,000,000、Indirect Cost: ¥6,000,000)
Fiscal Year 2020: ¥24,050,000 (Direct Cost: ¥18,500,000、Indirect Cost: ¥5,550,000)
Fiscal Year 2019: ¥33,800,000 (Direct Cost: ¥26,000,000、Indirect Cost: ¥7,800,000)
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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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