Machine Learning Phases of Strongly Correlated Fermions
Machine learning offers an unprecedented perspective for the problem of classifying phases in condensed matter physics. We employ neural-network machine learning techniques to distinguish finite-temperature phases of the strongly correlated fermions on cubic lattices. We show that a three-dimensiona...
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| Huvudupphov: | , , , |
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| Materialtyp: | Artigo |
| Språk: | Inglês |
| Utgiven: |
American Physical Society
2017-08-01
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| Serie: | Physical Review X |
| Länkar: | http://doi.org/10.1103/PhysRevX.7.031038 |
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