  In traditional finite-temperature Kohn-Sham density functional theory
(KSDFT), the well-known orbitals wall restricts the use of first-principles
molecular dynamics methods at extremely high temperatures. However, stochastic
density functional theory (SDFT) can overcome the limitation. Recently, SDFT
and its related mixed stochastic-deterministic density functional theory, based
on the plane-wave basis set, have been implemented in the first-principles
electronic structure software ABACUS [Phys. Rev. B 106, 125132(2022)]. In this
study, we combine SDFT with the Born-Oppenheimer molecular dynamics (BOMD)
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. Consequently, we compute and
analyze the structural properties, dynamic properties, and transport
coefficients of warm dense matter. The abovementioned methods offer a new
approach with first-principles accuracy to tackle various properties of warm
dense matter.
