| Budget Amount *help |
¥44,980,000 (Direct Cost: ¥34,600,000、Indirect Cost: ¥10,380,000)
Fiscal Year 2023: ¥7,930,000 (Direct Cost: ¥6,100,000、Indirect Cost: ¥1,830,000)
Fiscal Year 2022: ¥7,930,000 (Direct Cost: ¥6,100,000、Indirect Cost: ¥1,830,000)
Fiscal Year 2021: ¥11,050,000 (Direct Cost: ¥8,500,000、Indirect Cost: ¥2,550,000)
Fiscal Year 2020: ¥7,930,000 (Direct Cost: ¥6,100,000、Indirect Cost: ¥1,830,000)
Fiscal Year 2019: ¥10,140,000 (Direct Cost: ¥7,800,000、Indirect Cost: ¥2,340,000)
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| Outline of Final Research Achievements |
We have established the foundation (material database, theory and methodology of machine learning) of materials informatics for various material systems such as polymeric materials, inorganic compounds, and quasiperiodic materials. In particular, to overcome the problem of insufficient data resources, which is the biggest obstacle in data-driven materials research, we have promoted the integration of machine learning and computer experiments such as molecular dynamics simulations, the development of Sim2Real transfer learning methods for integrated analysis of heterogeneous data from real-world and computer experiments, and the development of materials database. We have also applied these methodologies to discover new materials for various material systems (quasicrystals, highly thermally conductive amorphous polymers, polymer liquid crystals, etc.), thus demonstrating the concept of materials informatics.
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