Accelerated Continuous time quantum Monte Carlo method with Machine Learning
| Autor: | Hunpyo Lee, Taegeun Song |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Physics
Time quantum Strongly Correlated Electrons (cond-mat.str-el) business.industry Monte Carlo method FOS: Physical sciences 02 engineering and technology 021001 nanoscience & nanotechnology Machine learning computer.software_genre 01 natural sciences Omega Condensed Matter - Strongly Correlated Electrons Dynamical mean field theory 0103 physical sciences Artificial intelligence 010306 general physics 0210 nano-technology business computer |
| DOI: | 10.48550/arxiv.1901.01501 |
| Popis: | An acceleration of continuous time quantum Monte Carlo (CTQMC) methods is a potentially interesting branch of work as they are matchless as impurity solvers of a density functional theory in combination with a dynamical mean field theory approach for the description of electronic structures of strongly correlated materials. The inversion of the $k \times k$ matrix given by the diagram expansion order $k$ in the CTQMC update and the multiplication of the $k \times k$ matrix and the non-interacting Green's function to measure the impurity Green's function are computationally time-consuming. Here, we propose the CTQMC method in combination with a machine learning technique, which would eliminate the need for multiplication of the matrix with the non-interacting Green's function. This method predicts the accurate impurity Green's function and double occupancy at low temperature, and also considers the physical properties of high Matsubara frequency in a much shorter computational time than the conventional CTQMC method. Comment: 5 pages, 4 figures |
| Databáze: | OpenAIRE |
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