2022 Fiscal Year Final Research Report
Development of semiconductors with extraordinary strong light absorption based on Materials Informatics incorporating AI
| Project/Area Number |
19H02167
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Basic Section 21050:Electric and electronic materials-related
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| Research Institution | Gifu University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
志賀 元紀 岐阜大学, 工学部, 准教授 (20437263)
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| Project Period (FY) |
2019-04-01 – 2023-03-31
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| Keywords | 巨大光吸収 |
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| Outline of Final Research Achievements |
To find semiconductors exhibiting extraordinary strong light absorption by using machine learning and density function theory, we have developed a new optical-spectra calculation scheme (PHS method). In this case, the high-precision band gap calculation based on HSE06 approach becomes a limitation step. To achieve high throughput calculation within PHS, we have further developed a machine learning method (support vector regression analysis) that allows the fast and simple prediction of the band gap with the accuracy of ~0.2 eV. To generate AI training data incorporating various new materials, we performed a large scale first-principles calculations for I-II-V group semiconductors (a total of 250 crystals) and find promising solar cell materials of CaNaAs, BaKP, BaKAs.
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| Free Research Field |
半導体(太陽電池)
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| Academic Significance and Societal Importance of the Research Achievements |
マテリアルズインフォマティクスによる材料探索は、今後ますます多用されて行くと考えられる。但し、マテリアルズインフォマティクスによる大規模光学材料探索においては、限られた時間で多くの材料の光学物性を高いスループットで計算することが本質的に重要となる。本研究では、光学材料(特に太陽電池材料)の材料探索に特に必要となる第一原理計算手法および機械学習法を提案し、短時間で材料の光吸収係数を高精度で計算できる手法を確立した。さらに、太陽電池に適切なバンドギャップおよび光吸収係数を有し、資源的な制約の少ない新しい光学材料(CaNaAs, BaKP, BaKAs)を見出した。
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