Selflearning Monte Carlo method and cumulative update in fermion systems
In this study, we develop the selflearning Monte Carlo (SLMC) method, a generalpurpose numerical method recently introduced to simulate manybody 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 selflearned 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.
 Authors:

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 Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Department of Physics
 Chinese Academy of Sciences, Beijing (China). Institute of Physics
 Publication Date:
 Grant/Contract Number:
 SC0010526; SC0001299; FG0209ER46577
 Type:
 Accepted Manuscript
 Journal Name:
 Physical Review B
 Additional Journal Information:
 Journal Volume: 95; Journal Issue: 24; Journal ID: ISSN 24699950
 Publisher:
 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) (SC22). Materials Sciences & Engineering Division; USDOE
 Country of Publication:
 United States
 Language:
 English
 Subject:
 36 MATERIALS SCIENCE; 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; Magnetism; Monte Carlo methods; Numerical techniques; Quantum Monte Carlo; statistical physics
 OSTI Identifier:
 1424931
 Alternate Identifier(s):
 OSTI ID: 1361929
Liu, Junwei, Shen, Huitao, Qi, Yang, Meng, Zi Yang, and Fu, Liang. Selflearning Monte Carlo method and cumulative update in fermion systems. United States: N. p.,
Web. doi:10.1103/PhysRevB.95.241104.
Liu, Junwei, Shen, Huitao, Qi, Yang, Meng, Zi Yang, & Fu, Liang. Selflearning Monte Carlo method and cumulative update in fermion systems. United States. doi:10.1103/PhysRevB.95.241104.
Liu, Junwei, Shen, Huitao, Qi, Yang, Meng, Zi Yang, and Fu, Liang. 2017.
"Selflearning Monte Carlo method and cumulative update in fermion systems". United States.
doi:10.1103/PhysRevB.95.241104. https://www.osti.gov/servlets/purl/1424931.
@article{osti_1424931,
title = {Selflearning Monte Carlo method and cumulative update in fermion systems},
author = {Liu, Junwei and Shen, Huitao and Qi, Yang and Meng, Zi Yang and Fu, Liang},
abstractNote = {In this study, we develop the selflearning Monte Carlo (SLMC) method, a generalpurpose numerical method recently introduced to simulate manybody 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 selflearned 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.},
doi = {10.1103/PhysRevB.95.241104},
journal = {Physical Review B},
number = 24,
volume = 95,
place = {United States},
year = {2017},
month = {6}
}