Year: 2017
Type: article
Abstract: 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.
Authors: Junwei Liu, Huitao Shen, Yang Qi, Zi Yang Meng, Liang Fu
Language: English
Subfield: Condensed Matter Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Funders: David and Lucile Packard Foundation, U.S. Department of Energy, Massachusetts Institute of Technology, National Natural Science Foundation of China, Ministry of Science and Technology of the People's Republic of China, Office of Science, Solid-State Solar Thermal Energy Conversion, Massachusetts Institute of Technology, Basic Energy Sciences, Division of Materials Sciences and Engineering
Awards: G1158654098, G1886441558, G1921736601, G222768804, 强关联电子系统中的量子蒙特卡洛模拟, G29355420, PREDICTIVE THEORY OF TOPOLOGICAL STATES OF MATTER, G3333241549, G4765361774, G5197807838, G5648016710, NEW: SOLID-STATE SOLAR-THERMAL ENERGY CONVERSION CENTER (S3TEC CENTER) -- EFRC; PI - GANG CHEN, G6033486225, NEW: SOLID-STATE SOLAR-THERMAL ENERGY CONVERSION CENTER (S3TEC CENTER) -- EFRC; PI - GANG CHEN, G7148289708, G7339167617, G8765236518, G906456538, G929121700
OpenAlex ID: W2557778781