Machine Learning Out-of-Equilibrium Phases of Matter
Abstract
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.
- Authors:
-
- Cornell Univ., Ithaca, NY (United States). Dept. of Physics
- Harvard Univ., Cambridge, MA (United States). Dept. of Physics
- Publication Date:
- Research Org.:
- Cornell Univ., Ithaca, NY (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1541329
- Alternate Identifier(s):
- OSTI ID: 1456267
- Grant/Contract Number:
- SC0010313
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- Physical Review Letters
- Additional Journal Information:
- Journal Volume: 120; Journal Issue: 25; Journal ID: ISSN 0031-9007
- Publisher:
- American Physical Society (APS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY
Citation Formats
Venderley, Jordan, Khemani, Vedika, and Kim, Eun-Ah. Machine Learning Out-of-Equilibrium Phases of Matter. United States: N. p., 2018.
Web. doi:10.1103/physrevlett.120.257204.
Venderley, Jordan, Khemani, Vedika, & Kim, Eun-Ah. Machine Learning Out-of-Equilibrium Phases of Matter. United States. https://doi.org/10.1103/physrevlett.120.257204
Venderley, Jordan, Khemani, Vedika, and Kim, Eun-Ah. 2018.
"Machine Learning Out-of-Equilibrium Phases of Matter". United States. https://doi.org/10.1103/physrevlett.120.257204. https://www.osti.gov/servlets/purl/1541329.
@article{osti_1541329,
title = {Machine Learning Out-of-Equilibrium Phases of Matter},
author = {Venderley, Jordan and Khemani, Vedika and Kim, Eun-Ah},
abstractNote = {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.},
doi = {10.1103/physrevlett.120.257204},
url = {https://www.osti.gov/biblio/1541329},
journal = {Physical Review Letters},
issn = {0031-9007},
number = 25,
volume = 120,
place = {United States},
year = {Thu Jun 21 00:00:00 EDT 2018},
month = {Thu Jun 21 00:00:00 EDT 2018}
}
Web of Science
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