Title | Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter |
Authors | Chen, Tao Liu, Qianrui Liu, Yu Sun, Liang Chen, Mohan |
Affiliation | Peking Univ, Coll Engn, HEDPS, CAPT, Beijing 100871, Peoples R China Peking Univ, Sch Phys, Beijing 100871, Peoples R China |
Keywords | STATIC STRUCTURE FACTOR IRREVERSIBLE-PROCESSES APPROXIMATION |
Issue Date | 1-Jan-2024 |
Publisher | MATTER AND RADIATION AT EXTREMES |
Abstract | In traditional finite-temperature Kohn-Sham density functional theory (KSDFT), the partial occupation of a large number of high-energy KS eigenstates restricts the use of first-principles molecular dynamics methods at extremely high temperatures. However, stochastic density functional theory (SDFT) can overcome this limitation. Recently, SDFT and the related mixed stochastic-deterministic density functional theory, based on a plane-wave basis set, have been implemented in the first-principles electronic structure software ABACUS [Q. Liu and M. Chen, Phys. Rev. B 106, 125132 (2022)]. In this study, we combine SDFT with the Born-Oppenheimer molecular dynamics method to investigate systems with temperatures ranging from a few tens of eV to 1000 eV. Importantly, we train machine-learning-based interatomic models using the SDFT data and employ these deep potential models to simulate large-scale systems with long trajectories. Subsequently, we compute and analyze the structural properties, dynamic properties, and transport coefficients of warm dense matter. |
URI | http://hdl.handle.net/20.500.11897/694438 |
ISSN | 2468-2047 |
DOI | 10.1063/5.0163303 |
Indexed | SCI(E) |
Appears in Collections: | 工学院 物理学院 |