Accurate high-throuput screening for mult-icomponent ionic conductors
| Project/Area Number |
17H04948
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| Research Category |
Grant-in-Aid for Young Scientists (A)
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| Allocation Type | Single-year Grants |
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| Research Field |
Physical properties of metals/Metal-base materials
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| Research Institution | Kyoto University |
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Principal Investigator |
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| 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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| Keywords | 固体イオニクス / プロトン伝導性酸化物 / ハイスループット・スクリーニング / 第一原理計算 |
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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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| Academic Significance and Societal Importance of the Research Achievements |
本研究では，多元系固体イオニクス材料に対するハイスループット・スクリーニングの実現に向けて，第一原理電子状態計算と機械学習を連携させた高速かつ高精度なイオン伝導解析手法の開発を行った．本手法を用いることで各種電気化学デバイスの材料開発を理論計算主導で行うことができ，デバイス開発の合理化・高速化が期待できる．また，本手法を用いて獲得したプロトン伝導性酸化物に関するデータをHPで公開することで，次世代燃料電池の開発が加速されることが期待される．
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Report
(4 results)
Research Products
(16 results)
