| Project/Area Number |
17K18763
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Condensed matter physics and related fields
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| Research Institution | Sophia University |
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Principal Investigator |
Ohtsuki Tomi 上智大学, 理工学部, 教授 (50201976)
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| Project Period (FY) |
2017-06-30 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥6,370,000 (Direct Cost: ¥4,900,000、Indirect Cost: ¥1,470,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥260,000 (Direct Cost: ¥200,000、Indirect Cost: ¥60,000)
Fiscal Year 2017: ¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
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| Keywords | 深層学習 / 機械学習 / 量子相転移 / トポロジカル系 / アンダーソン転移 / 量子パーコレーション / 畳み込みニューラルネットワーク / 相図 / ランダム系 / パーコレーション / アモルファス / recursive neural network / トポロジカル絶縁体 / ワイル半金属 / 量子カオス / トポロジカル物質 / ランダム電子系 / ニューラルネット |
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| Outline of Final Research Achievements |
Applications of neural networks to condensed matter physics are becoming popular and beginning to be well accepted. One of the applications is analyzing the wave functions and determining their quantum phases. We have used the multilayer convolutional neural network, so-called deep learning, to determine the quantum phases in random electron systems. After training the neural network by the supervised learning of wave functions in restricted parameter regions in known phases, the neural networks can determine the phases of the wave functions in wide parameter regions in unknown phases; hence, the phase diagrams are obtained. We demonstrate the validity and generality of this method by drawing the phase diagrams of two- and higher dimensional Anderson metal-insulator transitions and quantum percolations as well as disordered topological systems. Both real-space and Fourier space wave functions are analyzed. The advantages and disadvantages over conventional methods are discussed.
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| Academic Significance and Societal Importance of the Research Achievements |
機械学習，広くは人工知能の手法が，金属や半導体，絶縁体の性質を調べる固体物理においても有効性であることを示した。動物や人の画像認識として一般に親しまれている深層学習が，固体物理学に応用できることを示し，この手法の有効性を明らかにできた。
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