Data-driven approach to condensed-matter physics
| Project/Area Number |
25610102
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Mathematical physics/Fundamental condensed matter physics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Hukushima Koji 東京大学, 総合文化研究科, 准教授 (80282606)
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| Project Period (FY) |
2013-04-01 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2015: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2013: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 物性理論 / 磁性モデル / 磁化曲線 / 機械学習 / モンテカルロ法 / 中性子散乱 / 統計力学 / モデル化 / 計算物理 / 物性基礎論 / データ駆動 / アルゴリズム / ベイズ推論 / 画像データ |
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| Outline of Final Research Achievements |
Our main purpose in this project is to develop a data-driven method for materials science. We first propose a systematic method based on the machine learning in which a theoretical model with magnetic interactions is established from an input data of magnetization curve. The proposed method enables us to infer a suitable model among many candidates. Consequently, one may obtain microscopic spin structure which is difficult to see in a conventional experiments and it provides useful information on a following experiment design. It is also found to be efficient to use a Bayesian optimization for quantum spin systems. Meanwhile, dealing with the possibility of handling a big data from large experimental facilities, we develop an inference method of a relaxation-time distribution from neutron-diffraction experiments and offer a new direction including real-data analyses.
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Report
(4 results)
Research Products
(18 results)
