Silicon_NN_PotentialES.pdf (2.67 MB)
Silicon potentials investigated using density functional theory fitted neural networks
journal contributionposted on 2014-07-30, 14:07 authored by E. Sanville, Ajeevsing Bholoa, Roger Smith, Steven KennySteven 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.
- Mathematical Sciences
Published inJOURNAL OF PHYSICS-CONDENSED MATTER
Pages? - ? (10)
CitationSANVILLE, 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
- SMUR (Submitted Manuscript Under Review)
NotesThis 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