Please use this identifier to cite or link to this item: https://repositorio.ufba.br/handle/ri/7907
metadata.dc.type: Artigo de Periódico
Title: The fitting of potential energy and transition moment functions using neural networks: transition probabilities in OH (A2Rþ ! X2P)
Other Titles: Chemical Physics
Authors: Bitencourt, Ana Carla Peixoto
Prudente, Frederico Vasconcellos
Viana, Jose David Mangueira
metadata.dc.creator: Bitencourt, Ana Carla Peixoto
Prudente, Frederico Vasconcellos
Viana, Jose David Mangueira
Abstract: We have studied the performance of the back-propagation neural network with different architectures and activation functions to fit potential energy curves and dipolar transition moment functions of the OH molecule from the ab initio data points of Bauschlicher and Langhoff [J. Chem. Phys. 87 (1987) 4665]. The neural network fittings are tested in different moments of the training process by computing the vibrational levels, the transition probabilities between A2Rþ and X2P electronic states, and the radiative lifetimes. The results from the neural network fittings are then compared with experimental values, previous results calculated by Bauschlicher and Langhoff and the ones obtained by using of extended Rydberg function fitting.
Keywords: Neural networks
Back-propagation
Discrete variable representation
Potential energy surfaces
Transition probabilities
OH molecule
Publisher: Elservier
URI: http://www.repositorio.ufba.br/ri/handle/ri/7907
Issue Date: 2004
Appears in Collections:Artigo Publicado em Periódico (FIS)

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