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Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks
Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks
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Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks

Pukrittayakamee, A. ; Malshe, M. ; Hagan, M. ; Raff, L. M. ; Narulkar, R. ; Bukkapatnum, S. ; Komanduri, R.

The Journal of chemical physics, 2009-04, Vol.130 (13), p.134101-134101-10 [Periódico revisado por pares]

United States: American Institute of Physics

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2
Development of generalized potential-energy surfaces using many-body expansions, neural networks, and moiety energy approximations
Development of generalized potential-energy surfaces using many-body expansions, neural networks, and moiety energy approximations
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Development of generalized potential-energy surfaces using many-body expansions, neural networks, and moiety energy approximations

Malshe, M. ; Narulkar, R. ; Raff, L. M. ; Hagan, M. ; Bukkapatnam, S. ; Agrawal, P. M. ; Komanduri, R.

The Journal of chemical physics, 2009-05, Vol.130 (18), p.184102-184102-9 [Periódico revisado por pares]

United States: American Institute of Physics

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3
Parametrization of analytic interatomic potential functions using neural networks
Parametrization of analytic interatomic potential functions using neural networks
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Parametrization of analytic interatomic potential functions using neural networks

Malshe, M ; Narulkar, R ; Raff, L M ; Hagan, M ; Bukkapatnam, S ; Komanduri, R

The Journal of chemical physics, 2008-07, Vol.129 (4), p.044111-044111 [Periódico revisado por pares]

United States

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4
Ab initio potential-energy surfaces for complex, multichannel systems using modified novelty sampling and feedforward neural networks
Ab initio potential-energy surfaces for complex, multichannel systems using modified novelty sampling and feedforward neural networks
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Ab initio potential-energy surfaces for complex, multichannel systems using modified novelty sampling and feedforward neural networks

Raff, L M ; Malshe, M ; Hagan, M ; Doughan, D I ; Rockley, M G ; Komanduri, R

The Journal of chemical physics, 2005-02, Vol.122 (8), p.84104-84104 [Periódico revisado por pares]

United States

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5
Molecular dynamics investigations of the dissociation of SiO2 on an ab initio potential energy surface obtained using neural network methods
Molecular dynamics investigations of the dissociation of SiO2 on an ab initio potential energy surface obtained using neural network methods
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Molecular dynamics investigations of the dissociation of SiO2 on an ab initio potential energy surface obtained using neural network methods

Agrawal, Paras M ; Raff, Lionel M ; Hagan, Martin T ; Komanduri, Ranga

The Journal of chemical physics, 2006-04, Vol.124 (13), p.134306-134306 [Periódico revisado por pares]

United States

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6
Accurate prediction of higher-level electronic structure energies for large databases using neural networks, Hartree-Fock energies, and small subsets of the database
Accurate prediction of higher-level electronic structure energies for large databases using neural networks, Hartree-Fock energies, and small subsets of the database
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Accurate prediction of higher-level electronic structure energies for large databases using neural networks, Hartree-Fock energies, and small subsets of the database

Malshe, M. ; Pukrittayakamee, A. ; Raff, L. M. ; Hagan, M. ; Bukkapatnam, S. ; Komanduri, R.

The Journal of chemical physics, 2009-09, Vol.131 (12), p.124127-124127-12 [Periódico revisado por pares]

United States: American Institute of Physics

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7
Molecular dissociation of hydrogen peroxide (HOOH) on a neural network ab initio potential surface with a new configuration sampling method involving gradient fitting
Molecular dissociation of hydrogen peroxide (HOOH) on a neural network ab initio potential surface with a new configuration sampling method involving gradient fitting
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Molecular dissociation of hydrogen peroxide (HOOH) on a neural network ab initio potential surface with a new configuration sampling method involving gradient fitting

Le, Hung M. ; Huynh, Sau ; Raff, Lionel M.

The Journal of chemical physics, 2009-07, Vol.131 (1), p.014107-014107-10 [Periódico revisado por pares]

United States: American Institute of Physics

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8
Cis-->trans, trans-->cis isomerizations and N-O bond dissociation of nitrous acid (HONO) on an ab initio potential surface obtained by novelty sampling and feed-forward neural network fitting
Cis-->trans, trans-->cis isomerizations and N-O bond dissociation of nitrous acid (HONO) on an ab initio potential surface obtained by novelty sampling and feed-forward neural network fitting
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Cis-->trans, trans-->cis isomerizations and N-O bond dissociation of nitrous acid (HONO) on an ab initio potential surface obtained by novelty sampling and feed-forward neural network fitting

Le, Hung M ; Raff, Lionel M

The Journal of chemical physics, 2008-05, Vol.128 (19), p.194310-194310 [Periódico revisado por pares]

United States

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9
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 method
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 method
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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 method

Malshe, M. ; Raff, L. M. ; Rockley, M. G. ; Hagan, M. ; Agrawal, Paras M. ; Komanduri, R.

The Journal of chemical physics, 2007-10, Vol.127 (13), p.134105-134105-7 [Periódico revisado por pares]

United States: American Institute of Physics

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10
Input vector optimization of feed-forward neural networks for fitting ab initio potential-energy databases
Input vector optimization of feed-forward neural networks for fitting ab initio potential-energy databases
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Input vector optimization of feed-forward neural networks for fitting ab initio potential-energy databases

Malshe, M. ; Raff, L. M. ; Hagan, M. ; Bukkapatnam, S. ; Komanduri, R.

The Journal of chemical physics, 2010-05, Vol.132 (20), p.204103-204103-8 [Periódico revisado por pares]

United States: American Institute of Physics

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