Learning phase transitions by confusion
Abstract
A neural-network technique can exploit the power of machine learning to mine the exponentially large data sets characterizing the state space of condensed-matter systems. Topological transitions and many-body localization are first on the list.
- Publication:
-
Nature Physics
- Pub Date:
- May 2017
- DOI:
- 10.1038/nphys4037
- arXiv:
- arXiv:1610.02048
- Bibcode:
- 2017NatPh..13..435V
- Keywords:
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- Condensed Matter - Disordered Systems and Neural Networks;
- Condensed Matter - Strongly Correlated Electrons;
- Physics - Computational Physics
- E-Print:
- 5 pages, 3 figures