| Project/Area Number |
22KJ0627
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| Project/Area Number (Other) |
21J21382 (2021-2022)
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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 (2021-2022) |
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| Section | 国内 |
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| Review Section |
Basic Section 19020:Thermal engineering-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
DING WENYANG 東京大学, 工学系研究科, 特別研究員(DC1)
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| Project Period (FY) |
2023-03-08 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 2023: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2022: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2021: ¥800,000 (Direct Cost: ¥800,000)
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| Keywords | thermal conductivity / machine learning / global distribution / features extraction / VdW heterostructures / Thermal conductivity / Phonon incident angle / vdW heterostructures / Materials informatics / Bayesian optimization |
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| Outline of Research at the Start |
Minimize thermal conductivity of low dimensional materials by materials informatics and analyze the underlying physical mechanism from the dependence of mode-resolved phonon transmission on incident angle.
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| Outline of Annual Research Achievements |
By combining Explainable Artificial Intelligence (XAI) principles and self-learning entropic population annealing (SLEPA) method, we can efficiently explore global distribution while ensuring outputs are explainable and transparent. In detail, we first validated the effectiveness of SLEPA by comparing the 10-layer graphene-WS2 heterostructure’s thermal conductivity distribution among ground truth, SLEPA, Bayesian optimization and random sampling. Then, we performed SLEPA on 14-layer graphene-WS2 heterostructures. Moreover, we extracted three features which could suppress phonon transmission across the full range of frequency and angle of incidence. Finally, we constructed an empirical model which could predict thermal conductivity of graphene-WS2 heterostructure with 70% accuracy.
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