  We introduce a practical hybrid approach that combines orbital-free density
functional theory (DFT) with Kohn-Sham DFT for speeding up first-principles
molecular dynamics simulations. Equilibrated ionic configurations are generated
using orbital-free DFT for subsequent Kohn-Sham DFT molecular dynamics. This
leads to a massive reduction of the simulation time without any sacrifice in
accuracy. We assess this finding across systems of different sizes and
temperature, up to the warm dense matter regime. To that end, we use the cosine
distance between the time series of radial distribution functions representing
the ionic configurations. Likewise, we show that the equilibrated ionic
configurations from this hybrid approach significantly enhance the accuracy of
machine-learning models that replace Kohn-Sham DFT. Our hybrid scheme enables
systematic first-principles simulations of warm dense matter that are otherwise
hampered by the large numbers of atoms and the prevalent high temperatures.
Moreover, our finding provides an additional motivation for developing kinetic
and noninteracting free energy functionals for orbital-free DFT.
