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

The Journal of Chemical Physics
Paras M AgrawalRanga Komanduri

Abstract

The neural network (NN) procedure to interpolate ab initio data for the purpose of molecular dynamics (MD) simulations has been tested on the SiO(2) system. Unlike other similar NN studies, here, we studied the dissociation of SiO(2) without the initial use of any empirical potential. During the dissociation of SiO(2) into Si+O or Si+O(2), the spin multiplicity of the system changes from singlet to triplet in the first reaction and from singlet to pentet in the second. This paper employs four potential surfaces. The first is a NN fit [NN(STP)] to a database comprising the lowest of the singlet, triplet, and pentet energies obtained from density functional calculations in 6673 nuclear configurations. The other three potential surfaces are obtained from NN fits to the singlet, triplet, and pentet-state energies. The dissociation dynamics on the singlet-state and NN(STP) surfaces are reported. The results obtained using the singlet surface correspond to those expected if the reaction were to occur adiabatically. The dynamics on the NN(STP) surface represent those expected if the reaction follows a minimum-energy pathway. This study on a small system demonstrates the application of NNs for MD studies using ab initio data when the s...Continue Reading

References

Nov 4, 2000·Science·D H ZhangS Y Lee
Jan 1, 1994·IEEE Transactions on Neural Networks·M T Hagan, M B Menhaj

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Citations

Apr 6, 2013·The Journal of Physical Chemistry. a·Tobias Morawietz, Jörg Behler
May 20, 2011·Physical Chemistry Chemical Physics : PCCP·Roman M Balabin, Ekaterina I Lomakina
Nov 30, 2006·The Journal of Chemical Physics·Sergei Manzhos, Tucker Carrington
Aug 7, 2008·The Journal of Chemical Physics·M MalsheR Komanduri
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Oct 7, 2020·Chemical Reviews·Sergei Manzhos, Tucker Carrington
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