Molecular dynamics investigations of chlorine peroxide dissociation on a neural network ab initio potential energy surface

Abstract

Molecular dissociation of chlorine peroxide (ClOOCl), which consists of two elementary dissociation channels (of Cl–O and O–O), is investigated using molecular dynamics simulations on a neural network-fitted potential energy surface constructed by MP2 calculations with the 6-311G(d,p) basis set. When relaxed scans of the surface are executed, we observe that Cl–O dissociation is extremely reactive with a low barrier height of 0.1928 eV (18.602 kJ/mol), while O–O bond scission is less reactive (0.7164 eV or 69.122 kJ/mol). By utilizing the “novelty sampling” method, 35,006 data points in the ClOOCl configuration hyperspace are collected, and a 40-neuron feed-forward neural network is employed to fit approximately 90% of the data to produce an analytic potential energy function. The mean absolute error and root mean squared error of this fit are reported as 0.0078 eV (0.753 kJ/mol) and 0.0137 eV (1.322 kJ/mol), respectively. Finally, quasi-classical molecular dynamics is executed at various levels of internal energy (from 0.8 to 1.3 eV) to examine the bond ruptures. The two first-order rate coefficients are computed statistically, and the results range from 5.20 to 22.67 ps−1 for Cl–O dissociation and 3.72–8.35 ps−1 for O–O dissociation. Rice-Ramsperger-Kassel theory is utilized to classically correlate internal energies to rate coefficients in both cases, and the plots exhibit very good linearity, thus can be employed to predict rate coefficients at other internal energy levels with good reliability.