The best DFT functional is the ensemble of functionals

19 July 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

The development of better density functional theory (DFT) methods is one of the most active research areas, given the importance of DFT for ubiquitous molecular and materials simulations. However, this research primarily focuses on improving a specific exchange-correlation Kohn–Sham density functional. Here, we propose a robust procedure for constructing transferable ensembles of density functionals that perform superior to any constituent individual density functional. We show that such ensembles built only with the density functionals predating the GMTKN55 benchmark can reach a record-low weighted error of 1.69 kcal/mol on this benchmark compared to 3.08 kcal/mol of the best constituent density functional. We also introduce the DENS24 density functional ensembles as practical DFT methods with consistently accurate performance for various simulations at affordable cost. DENS24 ensembles will be available open-source and for simulations online as described at https://xacs.xmu.edu.cn/docs/mlatom/tutorial_dens.html.

Keywords

ensemble learning
DFT

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