Self-learning Monte Carlo method and cumulative update in fermion systems

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

doi:10.1103/PhysRevB.95.241104

Title: Self-learning Monte Carlo method and cumulative update in fermion systems

Journal Article · Wed Jun 07 00:00:00 EDT 2017 · Physical Review B

DOI:https://doi.org/10.1103/PhysRevB.95.241104· OSTI ID:1424931

Liu, Junwei ^[1]; Shen, Huitao ^[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

In this study, we develop the self-learning Monte Carlo (SLMC) method, a general-purpose numerical method recently introduced to simulate many-body systems, for studying interacting fermion systems. Our method uses a highly efficient update algorithm, which we design and dub “cumulative update”, to generate new candidate configurations in the Markov chain based on a self-learned bosonic effective model. From a general analysis and a numerical study of the double exchange model as an example, we find that the SLMC with cumulative update drastically reduces the computational cost of the simulation, while remaining statistically exact. Remarkably, its computational complexity is far less than the conventional algorithm with local updates.

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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; SC0001299; FG02-09ER46577

OSTI ID:: 1424931

Alternate ID(s):: OSTI ID: 1361929

Journal Information:: Physical Review B, Vol. 95, Issue 24; ISSN 2469-9950

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 61 works

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