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Accelerating Equilibration in First-Principles Molecular Dynamics with Orbital-Free Density Functional Theory

Fiedler, L.; Moldabekov, Z.; Shao, X.; Jiang, K.; Dornheim, T.; Pavanello, M.; Cangi, A.

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.

Keywords: Density Functional Theory; Machine Learning

Permalink: https://www.hzdr.de/publications/Publ-34778
Publ.-Id: 34778