Details

Autor(en) / Beteiligte

• Behler, Jörg
• Lorenz, Sönke
• Reuter, Karsten

Titel

Representing molecule-surface interactions with symmetry-adapted neural networks

Ist Teil von

• The Journal of chemical physics, 2007-07, Vol.127 (1), p.014705-014705

Ort / Verlag

United States

Erscheinungsjahr

2007

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.