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This content will become publicly available on June 7, 2018

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

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.
 [1] ;  [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:
SC0010526; SC0001299; FG02-09ER46577
Accepted Manuscript
Journal Name:
Physical Review B
Additional Journal Information:
Journal Volume: 95; Journal Issue: 24; 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
36 MATERIALS SCIENCE; 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; Magnetism; Monte Carlo methods; Numerical techniques; Quantum Monte Carlo; statistical physics
OSTI Identifier:
Alternate Identifier(s):
OSTI ID: 1361929