title: Representing molecule-surface interactions with symmetry-adapted neural networks.
abstract: The accurate description of molecule-surface interactions requires a detailed knowledge of the underlying potential-energy surface (PES). Recently, neural networks (NNs) have been shown to be an efficient technique to accurately interpolate the PES information provided for a set of molecular configurations, e.g., by first-principles calculations. Here, we further develop this approach by building the NN on a new type of symmetry functions, which allows to take the symmetry of the surface exactly into account. The accuracy and efficiency of such symmetry-adapted NNs is illustrated by the application to a six-dimensional PES describing the interaction of oxygen molecules with the Al(111) surface.
authors.affiliation: Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany.
authors.forename: Jörg
authors.initials: J
authors.lastname: Behler
authors.forename: Sönke
authors.initials: S
authors.lastname: Lorenz
authors.forename: Karsten
authors.initials: K
authors.lastname: Reuter