Perspective: Atomistic Simulations of Water and Aqueous Systems with Machine Learning Potentials

TL;DR

This paper reviews the development and application of machine learning potentials for simulating water and aqueous systems, highlighting their ability to combine accuracy with efficiency and enabling studies of complex systems.

Contribution

It provides a comprehensive overview of the progress in using machine learning potentials for water simulations, from simple molecules to complex interfaces.

Findings

MLPs achieve high accuracy comparable to ab initio methods

MLPs significantly reduce computational cost of water simulations

Application of MLPs enables study of complex aqueous systems

Abstract

As the most important solvent, water has been at the center of interest since the advent of computer simulations. While early molecular dynamics and Monte Carlo simulations had to make use of simple model potentials to describe the atomic interactions, accurate ab initio molecular dynamics simulations relying on the first-principles calculation of the energies and forces have opened the way to predictive simulations of aqueous systems. Still, these simulations are very demanding, which prevents the study of complex systems and their properties. Modern machine learning potentials (MLPs) have now reached a mature state, allowing to overcome these limitations by combining the high accuracy of electronic structure calculations with the efficiency of empirical force fields. In this Perspective we give a concise overview about the progress made in the simulation of water and aqueous systems employing MLPs, starting from early work on free molecules and clusters via bulk liquid water to electrolyte solutions and solid-liquid interfaces.