Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter

T Chen and QR Liu and Y Liu and L Sun and MH Chen, MATTER AND RADIATION AT EXTREMES, 9, 015604 (2024).

DOI: 10.1063/5.0163303

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.

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