Self-learning continuous-time Monte Carlo method in strongly correlated systems
| Project/Area Number |
18K03552
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 13030:Magnetism, superconductivity and strongly correlated systems-related
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| Research Institution | Japan Atomic Energy Agency |
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Principal Investigator |
Nagai Yuki 国立研究開発法人日本原子力研究開発機構, システム計算科学センター, 副主任研究員 (20587026)
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2020: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
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| Keywords | 自己学習モンテカルロ法 / 機械学習 / 強相関電子系 / モンテカルロ法 / 量子モンテカルロ |
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| Outline of Final Research Achievements |
We applied the self-learning Monte Carlo method to various kinds of fields, such as electron systems, molecular simulations, lattice quantum chromodynamics. For example, in the field of the molecular simulations, we developed self-learning hybrid Monte Carlo method, which is one of the best tools to generate very accurate neural network potentials.
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| Academic Significance and Societal Importance of the Research Achievements |
自己学習モンテカルロ法を様々な分野へと適用できることを示した。特に原子分子シミュレーション分野における自己学習ハイブリッドモンテカルロ法は、第一原理分子動力学計算と呼ばれる材料物性分野において非常に重要なシミュレーションを大幅に高速化することが可能であり、学術的な重要性に加えて産業界への応用可能性も考えられる。
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Report
(5 results)
Research Products
(20 results)
