Silicon_NN_PotentialES.pdf (2.67 MB)
Silicon potentials investigated using density functional theory fitted neural networks
journal contribution
posted on 2014-07-30, 14:07 authored by E. Sanville, Ajeevsing Bholoa, Roger Smith, Steven KennySteven KennyWe 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 MATTERVolume
20Issue
28Pages
? - ? (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 LtdVersion
- SMUR (Submitted Manuscript Under Review)
Publication date
2008Notes
This article was published in the serial, Journal of Physics: Condensed Matter [© IOP Publishing]. The definitive version is available at: http://dx.doi.org/10.1088/0953-8984/20/28/285219ISSN
0953-8984Publisher version
Language
- en