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The Journal of Chemical Physics|June 3, 2010
Input vector optimization of feed-forward neural networks for fitting ab initio potential-energy databasesM Malshe, L M Raff, M Hagan, et al.
The Journal of Chemical Physics|October 2, 2009
Accurate prediction of higher-level electronic structure energies for large databases using neural networks, Hartree-Fock energies, and small subsets of the databaseM Malshe, A Pukrittayakamee, L M Raff, et al.
The Journal of Chemical Physics|August 7, 2008
Parametrization of analytic interatomic potential functions using neural networksM Malshe, R Narulkar, L M Raff, et al.
Cancer|August 15, 1983
Angiosarcoma of the superior vena cavaR P Abratt, M Williams, M Raff, et al.
The American Journal of the Medical Sciences|November 1, 1976
A double blind study of the effects of zinc sulfate on taste and smell dysfunctionR I Henkin, P J Schecter, W T Friedewald, et al.
The Journal of Chemical Physics|October 9, 2007
Theoretical investigation of the dissociation dynamics of vibrationally excited vinyl bromide on an ab initio potential-energy surface obtained using modified novelty sampling and feedforward neural networks. II. Numerical application of the methodM Malshe, L M Raff, M G Rockley, et al.
The Journal of Chemical Physics|April 20, 2005
Ab initio potential-energy surfaces for complex, multichannel systems using modified novelty sampling and feedforward neural networksL M Raff, M Malshe, M Hagan, et al.
The Journal of Chemical Physics|April 10, 2009
Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networksA Pukrittayakamee, M Malshe, M Hagan, et al.
The Journal of Chemical Physics|February 14, 2006
Theoretical investigation of the dissociation dynamics of vibrationally excited vinyl bromide on an ab initio potential-energy surface obtained using modified novelty sampling and feed-forward neural networksD I Doughan, L M Raff, M G Rockley, et al.
The Journal of Chemical Physics|May 20, 2009
Development of generalized potential-energy surfaces using many-body expansions, neural networks, and moiety energy approximationsM Malshe, R Narulkar, L M Raff, et al.
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