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Machine learning the spectral function of a hole in a quantum antiferromagnet

Journal Article · · Physical Review. B
 [1];  [2];  [3]
  1. Rutgers University, New Brunswick, NJ (United States); Brookhaven National Laboratory (BNL), Upton, NY (United States). Condensed Matter Physics
  2. Brookhaven National Laboratory (BNL), Upton, NY (United States). Computational Science Center
  3. Brookhaven National Laboratory (BNL), Upton, NY (United States). Condensed Matter Physics
+ Show Author Affiliations
Understanding charge motion in a background of interacting quantum spins is a fundamental problem in quantum many-body physics. The most extensively studied model for this problem is the so-called t-t'-t''-J model, where the determination of the parameter t' in the context of cuprate superconductors is challenging. Here we present a theoretical study of the spectral functions of a mobile hole in the t-t'-t''-J model using two machine-learning techniques: K-nearest neighbor regression (KNN) and a feed-forward neural network (FFNN). We employ the self-consistent Born approximation to generate a dataset of about 1.3 x 105 spectral functions. Here we show that, for the forward problem, both methods allow for the accurate and efficient prediction of spectral functions, allowing, e.g., rapid searches through parameter space. Furthermore, we find that for the inverse problem (inferring Hamiltonian parameters from spectra), the FFNN can, but the KNN cannot, accurately predict the model parameters using merely the density of states. Our results suggest that it may be possible to use deep-learning methods to predict materials parameters from experimentally measured spectral functions.
Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Organization:
USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE); USDOE Office of Science (SC), Office of Workforce Development for Teachers & Scientists (WDTS)
Grant/Contract Number:
SC0012704
OSTI ID:
1974395
Alternate ID(s):
OSTI ID: 1973881
Report Number(s):
BNL-224422-2023-JAAM
Journal Information:
Physical Review. B, Journal Name: Physical Review. B Journal Issue: 20 Vol. 107; ISSN 2469-9950
Publisher:
American Physical Society (APS)Copyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

75 CONDENSED MATTER PHYSICS
SUPERCONDUCTIVITY AND SUPERFLUIDITY
Strongly correlated systems
machine learning
t-J model