| Project/Area Number |
23KF0250
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Multi-year Fund |
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| Section | 外国 |
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| Review Section |
Basic Section 13020:Semiconductors, optical properties of condensed matter and atomic physics-related
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| Research Institution | Kyoto University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
WEAL GEOFFREY 京都大学, 高等研究院, 外国人特別研究員
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| Project Period (FY) |
2023-11-15 – 2026-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 2025: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2024: ¥400,000 (Direct Cost: ¥400,000)
Fiscal Year 2023: ¥200,000 (Direct Cost: ¥200,000)
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| Keywords | Organic photovoltatics / Exciton diffusion / Excitonic coupling / First-principles / Data / Machine learning / Graph neural network |
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| Outline of Research at the Start |
有機太陽電池は地球温暖化対策として高く期待される。本研究では、計算・データ科学を統合する革新的な計算プラットフォームトを開発し、材料設計ルールをデータから導き出すことを目指す。具体的には、有機半導体における構造・物性値データベースを構築し、光－エネルギーの交換率が分子構造によってどのように影響されるか、また光－エネルギーの交換率を最大化するためにはどのように分子構造を設計すれば良いかを解明する。そこへ、脱炭素社会に必要な材料の発見とともに、材料科学研究の全体的なデジタル化を促進する。
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| Outline of Annual Research Achievements |
This project aims to accelerate exciton diffusion simulations in organic photovolatic materials using machine learning and data science. During FY2023, we started the following tasks: (i) creation of a database of organic semiconducting materials, including structure, transition amplitudes, and reorganization energies; (ii) creation of a preliminary graph neural network (GNN) model which can predict transition amplitudes via atomic transition charge predictions.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
The project is progressing exactly as planned.
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| Strategy for Future Research Activity |
During FY2024, we will complete our GNN model and verify it by comparing to experimental exciton diffusion values. We also aim to grasp the underlying physics of the GNN model by creating simplified theoretical models and performing sensitivity analyses.
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