Accelerating Equilibration in First-Principles Molecular Dynamics with Orbital-Free Density Functional Theory
Lenz Fiedler, Zhandos A. Moldabekov, Xuecheng Shao, Kaili Jiang,, Tobias Dornheim, Michele Pavanello, Attila Cangi

TL;DR
This paper presents a hybrid method combining orbital-free and Kohn-Sham DFT to accelerate first-principles molecular dynamics, significantly reducing simulation time while maintaining accuracy, especially useful for warm dense matter regimes.
Contribution
A practical hybrid approach that uses orbital-free DFT to generate equilibrated configurations for Kohn-Sham DFT, enhancing efficiency in molecular dynamics simulations.
Findings
Massive reduction in simulation time without accuracy loss
Improved accuracy of machine-learning models using hybrid configurations
Effective for systems up to warm dense matter conditions
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…
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Taxonomy
TopicsMachine Learning in Materials Science · Advanced Chemical Physics Studies · Spectroscopy and Quantum Chemical Studies
