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Keyword: liquids ×Clear all

1 claim from 1 paper

1. Materials Science › Machine Learning in Materials Science

   Atom-centered symmetry functions for constructing high-dimensional neural network potentials

   Behler · The Journal of Chemical Physics · 2011

   The paper discusses several types of atom-centred symmetry functions for building neural network potential-energy surfaces, using simple benchmark systems to examine their properties.

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   1. UncheckedThe paper states that its symmetry functions are general and can describe molecules, crystalline and amorphous solids, and liquids.“The symmetry functions are general and can be applied to all types of systems such as molecules, crystalline and amorphous solids, and liquids.”