Bulletin of the American Physical Society
APS March Meeting 2018
Monday–Friday, March 5–9, 2018; Los Angeles, California
Session F34: Machine Learning in Condensed Matter Physics II
11:15 AM–2:15 PM,
Tuesday, March 6, 2018
LACC Room: 409A
Sponsoring Units: DCOMP DCMP
Chair: Roger Melko, Univ of Waterloo
Abstract: F34.00002 : Machine learning out-of-equilibrium phases of matter*
11:51 AM–12:03 PM
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 or topological phases. 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 multi-partite phase boundaries. We find that this method outperforms conventional metrics (like the entanglement entropy) for identifying MBL phase transitions, revealing a sharper phase boundary and shedding new insight into 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.
*E-AK acknowledges DOE support under Award de-sc001031. JV acknowledges NSF support under Award NSF DMR-1308089. VK acknowleges support under the Harvard Society of Fellows and the William F. Milton Fund.
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