Elevating Density Functional Theory to Chemical Accuracy for Water Simulations through a Density-Corrected Many-Body Formalism

23 July 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Kohn-Sham density functional theory (DFT) has been extensively used to model the properties of water. Albeit maintaining a good balance between accuracy and efficiency, no density functional has so far achieved the degree of accuracy necessary to correctly predict the properties of water across the entire phase diagram. The recent development of the strongly constrained and appropriately normed (SCAN) functional has renewed the interest in ab initio simulations of liquid water, yielding promising results that are, however, still unable to reproduce all the experimental data. Here, we present density-corrected SCAN (DC-SCAN) calculations for water which, minimizing density-driven errors, elevate the accuracy of the SCAN functional to that of coupled cluster theory, the “gold standard” for chemical accuracy. Building upon the accuracy and efficiency of DC-SCAN within a many-body formal- ism, we introduce a data-driven many-body potential energy function, the MB-SCAN(DC) PEF, that is able to quantitatively reproduce coupled cluster reference values for interaction, binding, and individual many-body energies of water clusters. Importantly, the properties of liquid water calculated from molecular dynamics simulations carried out with the MB- SCAN(DC) PEF are found to be in excellent agreement with the experimental data, which thus demonstrates that MB-SCAN(DC) is effectively the first DFT-based model that correctly describes water from the gas to the condensed phase. Since the many-body formalism adopted by the present MB-SCAN(DC) PEF for water is general, we believe it can open the door to the routine development of data-driven many-body PEFs for predictive simulations of generic (small) molecules in the gas, liquid, and solid phases.


density functional theory
many-body interactions
chemical accuracy
machine learning

Supplementary materials

Supplementary Information
Additional analyses of many-body interactions in water clusters and comparisons of radial distribution functions of liquid water.


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