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Representing molecule-surface interactions with symmetry-adapted neural networks

Jorg Behler·Sonke Lorenz·Karsten Reuter
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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.

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Jorg Behler, Sonke Lorenz, Karsten Reuter. 2007-02-22. Representing molecule-surface interactions with symmetry-adapted neural networks. https://doi.org/10.1063/1.2746232

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