Machine learning phases of matter
Abstract
The success of machine learning techniques in handling big data sets proves ideal for classifying condensed-matter phases and phase transitions. The technique is even amenable to detecting non-trivial states lacking in conventional order.
- Publication:
-
Nature Physics
- Pub Date:
- May 2017
- DOI:
- 10.1038/nphys4035
- arXiv:
- arXiv:1605.01735
- Bibcode:
- 2017NatPh..13..431C
- Keywords:
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- Condensed Matter - Strongly Correlated Electrons
- E-Print:
- 18 pages, 8 figures, 1 table