2023 Fiscal Year Annual Research Report
粒子と波動の二重性を利用した低次元物質のフォノン輸送のフルスペクトル制御
| Project/Area Number |
22KJ0627
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| Allocation Type | Multi-year Fund |
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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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| Keywords | thermal conductivity / machine learning / global distribution / features extraction |
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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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Research Products
(2 results)
