Abstract
A previously reported method for conducting molecular dynamics simulations of gas-phase chemical dynamics on ab initio potential-energy surfaces using modified novelty sampling and feedforward neural networks is applied to the investigation of the unimolecular dissociation of vinyl bromide. The neural network is fitted to a database comprising the MP4(SDQ) energies computed for 71 969 nuclear configurations using an extended basis set. Dissociation rate coefficients and branching ratios at an internal excitation energy of 6.44eV for all six open reaction channels are reported. The distribution of vibrational energy in HBr formed in three-center dissociation is computed and found to be in excellent accord with experimental measurements. Computational requirements for the electronic structure calculations, neural network training, and trajectory calculations are given. The weight and bias matrices required for implementation of the neural network potential are made available through the Supplementary Material.
Bibliography
Malshe, M., Raff, L. M., Rockley, M. G., Hagan, M., Agrawal, P. M., & Komanduri, R. (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 method. The Journal of Chemical Physics, 127(13).
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Dates
Type | When |
---|---|
Created | 17 years, 10 months ago (Oct. 4, 2007, 6:37 p.m.) |
Deposited | 2 years, 1 month ago (July 15, 2023, 2:54 a.m.) |
Indexed | 3 weeks, 4 days ago (July 30, 2025, 6:49 a.m.) |
Issued | 17 years, 10 months ago (Oct. 4, 2007) |
Published | 17 years, 10 months ago (Oct. 4, 2007) |
Published Online | 17 years, 10 months ago (Oct. 4, 2007) |
Published Print | 17 years, 10 months ago (Oct. 7, 2007) |
@article{Malshe_2007, title={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}, volume={127}, ISSN={1089-7690}, url={http://dx.doi.org/10.1063/1.2768948}, DOI={10.1063/1.2768948}, number={13}, journal={The Journal of Chemical Physics}, publisher={AIP Publishing}, author={Malshe, M. and Raff, L. M. and Rockley, M. G. and Hagan, M. and Agrawal, Paras M. and Komanduri, R.}, year={2007}, month=oct }