| Budget Amount *help |
¥23,920,000 (Direct Cost: ¥18,400,000、Indirect Cost: ¥5,520,000)
Fiscal Year 2019: ¥6,760,000 (Direct Cost: ¥5,200,000、Indirect Cost: ¥1,560,000)
Fiscal Year 2018: ¥6,500,000 (Direct Cost: ¥5,000,000、Indirect Cost: ¥1,500,000)
Fiscal Year 2017: ¥10,660,000 (Direct Cost: ¥8,200,000、Indirect Cost: ¥2,460,000)
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| Outline of Final Research Achievements |
In the present study, we have proposed a combination technique of first-principles calculations and machine learning for analyzing ionic conductivity with both accuracy and efficiency, which realizes high-throughput screening for ionic conductors. Specifically, we have constructed a methodology to selectively evaluate the potential energy surface (PES) of charge carriers from the region of interest governing the ionic conductivity, by using Bayesian optimization (BO) based on Gaussian Process (GP) and Dynamic Programing (DP) for migration path search. In addition, we have found many possible candidates with high proton mobility by screening the inorganic crystal structure database using the proposed method.
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