Input vector optimization of feed-forward neural networks for fitting ab initio potential-energy databases.

Abstract

The variation in the fitting accuracy of neural networks (NNs) when used to fit databases comprising potential energies obtained from ab initio electronic structure calculations is investigated as a function of the number and nature of the elements employed in the input vector to the NN. Ab initio databases for H(2)O(2), HONO, Si(5), and H(2)C[Double Bond… (More)
DOI: 10.1063/1.3431624

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@article{Malshe2010InputVO, title={Input vector optimization of feed-forward neural networks for fitting ab initio potential-energy databases.}, author={Milind M Malshe and Lionel Raff and Martin Hagan and S. T. Bukkapatnam and Ranga Komanduri}, journal={The Journal of chemical physics}, year={2010}, volume={132 20}, pages={204103} }