• Rapid Communication

Self-learning Monte Carlo method

Junwei Liu, Yang Qi, Zi Yang Meng, and Liang Fu
Phys. Rev. B 95, 041101(R) – Published 4 January 2017
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Abstract

Monte Carlo simulation is an unbiased numerical tool for studying classical and quantum many-body systems. One of its bottlenecks is the lack of a general and efficient update algorithm for large size systems close to the phase transition, for which local updates perform badly. In this Rapid Communication, we propose a general-purpose Monte Carlo method, dubbed self-learning Monte Carlo (SLMC), in which an efficient update algorithm is first learned from the training data generated in trial simulations and then used to speed up the actual simulation. We demonstrate the efficiency of SLMC in a spin model at the phase transition point, achieving a 10–20 times speedup.

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  • Received 11 October 2016
  • Revised 5 December 2016

DOI:https://doi.org/10.1103/PhysRevB.95.041101

©2017 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied PhysicsStatistical Physics & Thermodynamics

Authors & Affiliations

Junwei Liu1,*, Yang Qi1, Zi Yang Meng2, and Liang Fu1,†

  • 1Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
  • 2Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China

  • *liujw@mit.edu
  • liangfu@mit.edu

See Also

Accelerated Monte Carlo simulations with restricted Boltzmann machines

Li Huang and Lei Wang
Phys. Rev. B 95, 035105 (2017)

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Vol. 95, Iss. 4 — 15 January 2017

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