Continuous Time Quantum Monte Carlo in Combination with Machine Learning on the Hubbard Model
| Autor: | Hunpyo Lee |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
010302 applied physics
Physics Hubbard model business.industry Computation Mott insulator Monte Carlo method General Physics and Astronomy 02 engineering and technology Electronic structure 021001 nanoscience & nanotechnology Machine learning computer.software_genre 01 natural sciences Lattice (order) 0103 physical sciences Strongly correlated material Density functional theory Artificial intelligence 0210 nano-technology business computer |
| Zdroj: | Journal of the Korean Physical Society. 75:841-844 |
| ISSN: | 1976-8524 0374-4884 |
| DOI: | 10.3938/jkps.75.841 |
| Popis: | The acceleration of exact continuous time quantum Monte Carlo (CTQMC) approaches in multi-site or multi-orbital systems is extremely interesting work, because these approaches are very time-consuming in terms of numerical computation and might account for the nature of exotic behaviors such as high-temperature superconductivity and Mott insulator behavior observed in the strongly correlated materials. We extend the recently developed interaction-expansion CTQMC method in combination with a machine learning (CTQMC+ML) approach for the single-site and single-orbital systems to multi-site and multi-orbital ones. This method can be applied to explore the nonlocal correlation effects in lattice models and to study the electronic structure of real materials via an ab-initio density functional theory plus dynamical mean field theory approach. We find that our CTQMC+ML method for multi-site (and multi-orbital) systems accurately predicts the impurity Green’s function with less computational time than the CTQMC approaches, as in the case of the single-site and single-orbital version. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink