Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials

Amir Omranpour, Pablo Montero De Hijes, Jörg Behler (Korresp. Autor*in), Christoph Dellago (Korresp. Autor*in)

Veröffentlichungen: Beitrag in FachzeitschriftArtikelPeer Reviewed


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 us 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.
FachzeitschriftJournal of Chemical Physics
PublikationsstatusVeröffentlicht - 7 Mai 2024

ÖFOS 2012

  • 103015 Kondensierte Materie
  • 103006 Chemische Physik
  • 103043 Computational Physics