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Title: Self-learning Monte Carlo method

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. Lastly, we demonstrate the efficiency of SLMC in a spin model at the phase transition point, achieving a 10–20 times speedup.
 [1] ;  [1] ;  [2] ;  [1]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Department of Physics
  2. Chinese Academy of Sciences, Beijing (China). Institute of Physics
Publication Date:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Physical Review B
Additional Journal Information:
Journal Volume: 95; Journal Issue: 4; Journal ID: ISSN 2469-9950
American Physical Society (APS)
Research Org:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division; USDOE
Country of Publication:
United States
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; Ising model; Machine learning; Markoavian processes; Monte Carlo methods; Stochastic analysis
OSTI Identifier:
Alternate Identifier(s):
OSTI ID: 1338104