Assessing the Interplay Between Functional-Driven and Density-Driven Errors in DFT Models of Water

17 January 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


We investigate the interplay between functional-driven and density-driven errors in different density functional theory (DFT) approximations, and the implications of these errors for simulations of water with DFT-based data-driven many-body potentials. Specifically, we quantify density-driven errors in two widely used dispersion-corrected functionals derived within the generalized gradient approximation (GGA), namely BLYP-D3 and revPBE-D3, and two modern meta-GGA functionals, namely SCAN and B97M-rV. The effects of functional-driven and density-driven errors on the interaction energies are assessed for the water clusters of the BEGDB dataset. Further insight into the nature of functional-driven errors is gained from applying the absolutely localized molecular orbital energy decomposition analysis (ALMO- EDA) to the interaction energies, which demonstrates that functional-driven errors are strongly correlated with the nature of the interactions. We discuss cases where density-corrected DFT (DC-DFT) models display higher accuracy than the original DFT models, and cases where reducing the density-driven errors leads to larger deviations from the reference energies due to the presence of large functional-driven errors. Finally, molecular dynamics simulations are performed with data-driven many-body potentials derived from DFT and DC-DFT data to determine the effect that minimizing density-driven errors has on the description of liquid water. Besides rationalizing the performance of widely used DFT models of water, we believe that our findings unveil fundamental relations between the shortcomings of some common DFT approximations and the requirements for accurate descriptions of molecular interactions, which will aid the development of a consistent, DFT-based framework for data-driven simulations of condensed-phase systems.


density functional theory
many-body expansion
data-driven models
self-interaction error
delocalization error
machine learning
functional-driven errors
density-driven errors

Supplementary materials

Supporting Information
Theoretical details about density-corrected many-body potential energy functions. Additional figures.


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