| Project/Area Number |
22KF0210
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| Project/Area Number (Other) |
22F22708 (2022)
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Multi-year Fund (2023)
Single-year Grants (2022) |
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| Section | 外国 |
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| Review Section |
Basic Section 60100:Computational science-related
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| Research Institution | Kyoto University |
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Principal Investigator |
鹿島 久嗣 京都大学, 情報学研究科, 教授 (80545583)
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| Co-Investigator(Kenkyū-buntansha) |
BARBOT ARMAND 京都大学, 情報学研究科, 外国人特別研究員
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| Project Period (FY) |
2023-03-08 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥1,600,000 (Direct Cost: ¥1,600,000)
Fiscal Year 2024: ¥400,000 (Direct Cost: ¥400,000)
Fiscal Year 2023: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 2022: ¥700,000 (Direct Cost: ¥700,000)
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| Keywords | マテリアルズインフォマティクス / 機械学習 / 人工知能 |
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| Outline of Research at the Start |
Nucleation of dislocations (defects in crystals responsible of plasticity) are essential to understand the deformation of nanocrystals. The objective of this project is to use machine learning (ML) to improve simulation methods of crystal plasticity at the mesoscale.
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| Outline of Annual Research Achievements |
This research enhances mesoscopic simulation of dislocation nucleation by integrating machine learning models trained on atomistic data. Initially flawed, the approach was refined into three parts: (1) A deterministic model predicts the first nucleation based on system shape and size. (2) A second model outputs strain interval distributions, informing subsequent nucleation timing. (3) A third model evaluates the likelihood of nucleation considering strain and energy, ensuring realistic simulation outcomes. These advancements were presented at MRS 2023 in Boston and MRM 2023 in Kyoto.
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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
A set of elemental technologies has been obtained through the research to date, and these have been presented at several international conferences. It is expected that the remaining research period will lead to final results integrating these technologies.
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| Strategy for Future Research Activity |
The fellow is preparing a paper for a peer-reviewed journal and collaborating with the counterpart lab in France to implement a mesoscopic simulation model. Concurrently, they're developing a Physically Informed Neural Network (PINN) to generate potential energy-strain curves for more effective nucleation criteria. This PINN model inputs system shape and size and outputs potential energy-strain curves, using them as nucleation criteria. The PINN approach, by applying physical constraints, significantly reduces data training needs. It assumes constant slope segments in potential energy-strain curves, specific to each system. Although the current model sometimes overestimates nucleations for certain shapes and sizes, it shows promise, and improvements are underway to enhance its accuracy.
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