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

Abstract

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.

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• From Density Response to Energy Functionals and Back: An ab initio perspective on Matter Under Extreme Conditions
• Averaging over atom snapshots in linear-response TDDFT of disordered systems: A case study of warm dense hydrogen
• Imposing Correct Jellium Response Is Key to Predict the Density Response by Orbital-Free DFT
• Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter
• Nonlocal vs Local Pseudopotentials Affect Kinetic Energy Kernels in Orbital-Free DFT
• Machine learning the electronic structure of matter across temperatures
• Adaptive Equilibration of Molecular Dynamics Simulations
• Unlocking the Power of Orbital-Free Density Functional Theory to Explore the Electronic Structure Under Extreme Conditions