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Silicon potentials investigated using density functional theory fitted neural networks

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journal contribution
posted on 30.07.2014, 14:07 by E. Sanville, Ajeevsing Bholoa, Roger Smith, Steven Kenny
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.

History

School

  • Science

Department

  • Mathematical Sciences

Published in

JOURNAL OF PHYSICS-CONDENSED MATTER

Volume

20

Issue

28

Pages

? - ? (10)

Citation

SANVILLE, E. ... et al, 2008. Silicon potentials investigated using density functional theory fitted neural networks. Journal of Physics: Condensed Matter, 20 (28), 285219.

Publisher

© IOP Publishing Ltd

Version

SMUR (Submitted Manuscript Under Review)

Publication date

2008

Notes

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

ISSN

0953-8984

Language

en

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Keywords

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