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In traditional finite-temperature Kohn-Sham density functional theory(KSDFT), the well-known orbitals wall restricts the use of first-principlesmolecular dynamics methods at extremely high temperatures. However, stochasticdensity functional theory (SDFT) can overcome the limitation. Recently, SDFTand its related mixed stochastic-deterministic density functional theory, basedon the plane-wave basis set, have been implemented in the first-principleselectronic structure software ABACUS [Phys. Rev. B 106, 125132(2022)]. In thisstudy, we combine SDFT with the Born-Oppenheimer molecular dynamics (BOMD)method to investigate systems with temperatures ranging from a few tens of eVto 1000 eV. Importantly, we train machine-learning-based interatomic modelsusing the SDFT data and employ these deep potential models to simulatelarge-scale systems with long trajectories. Consequently, we compute andanalyze the structural properties, dynamic properties, and transportcoefficients of warm dense matter. The abovementioned methods offer a newapproach with first-principles accuracy to tackle various properties of warmdense matter.

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