Perspective: Atomistic Simulations of Water and Aqueous Systems with Machine Learning Potentials
The Journal of Chemical Physics(2024)
摘要
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.
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