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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/15407

Title: Silicon potentials investigated using density functional theory fitted neural networks
Authors: Sanville, E.
Bholoa, Ajeevsing
Smith, Roger
Kenny, Steven D.
Issue Date: 2008
Publisher: © IOP Publishing Ltd
Citation: SANVILLE, E. ... et al, 2008. Silicon potentials investigated using density functional theory fitted neural networks. Journal of Physics: Condensed Matter, 20 (28), 285219.
Abstract: We present a method for fitting neural networks to geometric and energetic data sets. We then apply this method by fitting a neural network to a set of data generated using the local density approximation for systems composed entirely of silicon. In order to generate atomic potential energy data, we use the Bader analysis scheme to partition the total system energy among the constituent atoms. We then demonstrate the transferability of the neural network potential by fitting to various bulk, surface, and cluster systems.
Description: This article was published in the serial, Journal of Physics: Condensed Matter [© IOP Publishing]. The definitive version is available at: http://dx.doi.org/10.1088/0953-8984/20/28/285219
Version: Submitted for publication
DOI: 10.1088/0953-8984/20/28/285219
URI: https://dspace.lboro.ac.uk/2134/15407
Publisher Link: http://dx.doi.org/10.1088/0953-8984/20/28/285219
ISSN: 0953-8984
Appears in Collections:Published Articles (Maths)

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