Accurate Simulations of Water and Aqueous Solutions through Fine-Tuned Dispersion-Corrected Density Functional Theory and Machine-Learning Interatomic Potentials

TL;DR

This paper introduces a new method to improve the accuracy of simulations of water and aqueous solutions using advanced computational models and machine learning.

Contribution

A novel computational strategy is introduced to enhance DFT-D models and MLIPs for high-fidelity simulations of water and aqueous solutions.

Findings

The new MLIP accurately predicts various properties of water, including radial distribution functions and diffusion constants.

The method captures the anomalous behavior of water and improves agreement with experiments for MgCl2 hydration and water exchange dynamics.

Abstract

Dispersion-corrected density functional theory (DFT-D)
is widely
employed to model large molecular systems at an affordable computational
cost and to develop machine-learning interatomic potentials (MLIPs),
enabling reliable molecular dynamics (MD) simulations of condensed-phase
systems. Yet, given a molecular system, the choice of a specific DFT-D
model that can achieve the necessary accuracy over an extended range
of physicochemical properties and conditions is generally not trivial.
Here, we report an effective computational strategy for enhancing
the accuracy of standard DFT-D models toward high-level quantum mechanical
data and for developing MLIPs preserving the same high fidelity. Taking
water as a paradigmatic example, we derive a novel MLIP and demonstrate
that its use allows us to accurately predict a wide range of properties
in diverse forms, from small clusters to bulk liquid and ice, such
as radial distribution functions, fusion/vaporization enthalpies,
diffusion constants, and density isobars, capturing remarkably well
its peculiar and anomalous behavior, often elusive even to standard
first-principle MD simulations. Furthermore, we show how the same
computational strategy can be readily extended to treat aqueous solutions.
Considering MgCl2 in water as a test case, we develop a
MLIP and use it to predict the metal ion hydration structure and the
water exchange dynamics exhibiting a significantly improved agreement
with experiments with respect to both standard DFT-D and classical
force fields.

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