| Budget Amount *help |
¥44,980,000 (Direct Cost: ¥34,600,000、Indirect Cost: ¥10,380,000)
Fiscal Year 2017: ¥10,010,000 (Direct Cost: ¥7,700,000、Indirect Cost: ¥2,310,000)
Fiscal Year 2016: ¥11,570,000 (Direct Cost: ¥8,900,000、Indirect Cost: ¥2,670,000)
Fiscal Year 2015: ¥23,400,000 (Direct Cost: ¥18,000,000、Indirect Cost: ¥5,400,000)
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| Outline of Final Research Achievements |
We developed methodologies on first-principles thermodynamics calculations, compound features used for machine learning prediction of materials properties and kriging method to explore unknown materials. They are expected to support rational materials design from first principles calculations. We newly found new compounds with low thermal lattice conductivity from Bayesian optimization and expensive first-principles lattice thermal conductivity calculations. We also synthesized functional compound SnMoO4 starting from the prediction from first-principles calculations. Besides, we proposed a systematic set of compound features generated from elemental and structural representations. These frameworks can accelerate the discovery of unknown materials.
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