• Rapid Communication

Accelerating lattice quantum Monte Carlo simulations using artificial neural networks: Application to the Holstein model

Shaozhi Li, Philip M. Dee, Ehsan Khatami, and Steven Johnston
Phys. Rev. B 100, 020302(R) – Published 22 July 2019
PDFHTMLExport Citation

Abstract

Monte Carlo (MC) simulations are essential computational approaches with widespread use throughout all areas of science. We present a method for accelerating lattice MC simulations using fully connected and convolutional artificial neural networks that are trained to perform local and global moves in configuration space, respectively. Both networks take local spacetime MC configurations as input features and can, therefore, be trained using samples generated by conventional MC runs on smaller lattices before being utilized for simulations on larger systems. This approach is benchmarked for the case of determinant quantum Monte Carlo (DQMC) studies of the two-dimensional Holstein model. We find that both artificial neural networks are capable of learning an unspecified effective model that accurately reproduces the MC configuration weights of the original Hamiltonian and achieve an order of magnitude speedup over the conventional DQMC algorithm. Our approach is broadly applicable to many classical and quantum lattice MC algorithms.

  • Figure
  • Figure
  • Figure
  • Received 17 May 2019

DOI:https://doi.org/10.1103/PhysRevB.100.020302

©2019 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied PhysicsStatistical Physics & Thermodynamics

Authors & Affiliations

Shaozhi Li1,2, Philip M. Dee1, Ehsan Khatami3, and Steven Johnston1,4

  • 1Department of Physics and Astronomy, The University of Tennessee, Knoxville, Tennessee 37996, USA
  • 2Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA
  • 3Department of Physics and Astronomy, San Jose State University, San Jose, California 95192, USA
  • 4Joint Institute for Advanced Materials at the University of Tennessee, Knoxville, Tennessee 37996, USA

Article Text (Subscription Required)

Click to Expand

Supplemental Material (Subscription Required)

Click to Expand

References (Subscription Required)

Click to Expand
Issue

Vol. 100, Iss. 2 — 1 July 2019

Reuse & Permissions
Access Options
CHORUS

Article Available via CHORUS

Download Accepted Manuscript
Author publication services for translation and copyediting assistance advertisement

Authorization Required


×
×

Images

×

Sign up to receive regular email alerts from Physical Review B

Log In

Cancel
×

Search


Article Lookup

Paste a citation or DOI

Enter a citation
×