Self-learning Monte Carlo method

Liu, Junwei; Qi, Yang; Meng, Zi Yang; Fu, Liang

doi:10.1103/PhysRevB.95.041101

Title: Self-learning Monte Carlo method

Journal Article · Wed Jan 04 00:00:00 EST 2017 · Physical Review B

DOI:https://doi.org/10.1103/PhysRevB.95.041101· OSTI ID:1424928

Liu, Junwei ^[1]; Qi, Yang ^[1]; Meng, Zi Yang ^[2]; Fu, Liang ^[1]

Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Department of Physics
Chinese Academy of Sciences, Beijing (China). Institute of Physics

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.

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Research Organization:: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division; USDOE

Grant/Contract Number:: SC0010526

OSTI ID:: 1424928

Alternate ID(s):: OSTI ID: 1338104

Journal Information:: Physical Review B, Vol. 95, Issue 4; ISSN 2469-9950

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 150 works

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