Machine Learning Out-of-Equilibrium Phases of Matter

Venderley, Jordan; Khemani, Vedika; Kim, Eun-Ah

doi:10.1103/physrevlett.120.257204

Title: Machine Learning Out-of-Equilibrium Phases of Matter

Journal Article · Thu Jun 21 00:00:00 EDT 2018 · Physical Review Letters

DOI:https://doi.org/10.1103/physrevlett.120.257204· OSTI ID:1541329

Venderley, Jordan ^[1]; Khemani, Vedika ^[2]; Kim, Eun-Ah ^[1]

Cornell Univ., Ithaca, NY (United States). Dept. of Physics
Harvard Univ., Cambridge, MA (United States). Dept. of Physics

Neural-network-based machine learning is emerging as a powerful tool for obtaining phase diagrams when traditional regression schemes using local equilibrium order parameters are not available, as in many-body localized (MBL) or topological phases. Nevertheless, instances of machine learning offering new insights have been rare up to now. Here we show that a single feed-forward neural network can decode the defining structures of two distinct MBL phases and a thermalizing phase, using entanglement spectra obtained from individual eigenstates. For this, we introduce a simplicial geometry-based method for extracting multipartite phase boundaries. We find that this method outperforms conventional metrics for identifying MBL phase transitions, revealing a sharper phase boundary and shedding new insight on the topology of the phase diagram. Furthermore, the phase diagram we acquire from a single disorder configuration confirms that the machine-learning-based approach we establish here can enable speedy exploration of large phase spaces that can assist with the discovery of new MBL phases. To our knowledge, this Letter represents the first example of a standard machine learning approach revealing new information on phase transitions.

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Research Organization:: Cornell Univ., Ithaca, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0010313

OSTI ID:: 1541329

Alternate ID(s):: OSTI ID: 1456267

Journal Information:: Physical Review Letters, Vol. 120, Issue 25; ISSN 0031-9007

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 77 works

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