title: A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges.
abstract: Understanding the unique properties of water still represents a significant challenge for theory and experiment. Computer simulations by molecular dynamics require a reliable description of the atomic interactions, and in recent decades countless water potentials have been reported in the literature. Still, most of these potentials contain significant approximations, for instance a frozen internal structure of the individual water monomers. Artificial neural networks (NNs) offer a promising way for the construction of very accurate potential-energy surfaces taking all degrees of freedom explicitly into account. These potentials are based on electronic structure calculations for representative configurations, which are then interpolated to a continuous energy surface that can be evaluated many orders of magnitude faster. We present a full-dimensional NN potential for the water dimer as a first step towards the construction of a NN potential for liquid water. This many-body potential is based on environment-dependent atomic energy contributions, and long-range electrostatic interactions are incorporated employing environment-dependent atomic charges. We show that the potential and derived properties like vibrational frequencies are in excellent agreement with the underlying reference density-functional theory calculations.
authors.affiliation: Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, 44780 Bochum, Germany.
authors.forename: Tobias
authors.initials: T
authors.lastname: Morawietz
authors.forename: Vikas
authors.initials: V
authors.lastname: Sharma
authors.forename: Jörg
authors.initials: J
authors.lastname: Behler
mesh_terms: D019281:Dimerization; D056004:Molecular Dynamics Simulation; D016571:Neural Networks, Computer; D011789:Quantum Theory; D014867:Water / Q000737:chemistry*
chemical_list: D014867:Water