Learning phase transitions by confusion

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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.

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Most cited references 11

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Author and article information

Journal

Title: Nature Physics

Abbreviated Title: Nat Phys

Publisher: Springer Nature

ISSN (Print): 1745-2473

ISSN (Electronic): 1745-2481

Publication date Created: February 13 2017

Publication date (Print): February 13 2017

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Article

DOI: 10.1038/nphys4037

SO-VID: d63c0214-fdbe-4eb4-9432-078df875874b

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