| Budget Amount *help |
¥52,130,000 (Direct Cost: ¥40,100,000、Indirect Cost: ¥12,030,000)
Fiscal Year 2021: ¥11,700,000 (Direct Cost: ¥9,000,000、Indirect Cost: ¥2,700,000)
Fiscal Year 2020: ¥11,700,000 (Direct Cost: ¥9,000,000、Indirect Cost: ¥2,700,000)
Fiscal Year 2019: ¥11,700,000 (Direct Cost: ¥9,000,000、Indirect Cost: ¥2,700,000)
Fiscal Year 2018: ¥11,700,000 (Direct Cost: ¥9,000,000、Indirect Cost: ¥2,700,000)
Fiscal Year 2017: ¥5,330,000 (Direct Cost: ¥4,100,000、Indirect Cost: ¥1,230,000)
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| Outline of Final Research Achievements |
The use of machine learning for the development of highly functional materials is the main focus of materials informatics. However, for the safety of the materials development, it is necessary to explain the predicted functions of materials in terms of what humans can understand. In this study, we have investigated the relationship between the structure and function of a material, which are two essential elements of human understanding in materials science, and have shown that the three-dimensional structure of a material and the structure of a material suggested by its molecular formula can indeed be used to predict the physical properties that the material will exhibit. We also clarified the trade-off relation between computation time and computation accuracy for a solver of an optimization problem, which typically appears in the learning phase of machine learning. This is expected to lead to the construction of more efficient machine learning algorithms.
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