Representing molecule-surface interactions with symmetry-adapted neural networks

Details Representing molecule-surface interactions with symmetry-adapted neural networks Representing molecule-surface interactions with symmetry-adapted neural networks

2007-02-22

arxiv.org/abs/cond-mat/0702522v1

Physics

Condensed Matter

Materials Science

13 pages including 8 figures; related publications can be found at http://www.fhi-berlin.mpg.de/th/th.html

Scientific paper

10.1063/1.2746232

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.

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