High-Energy Reaction Dynamics of O$_3$

TL;DR

This study investigates the reaction dynamics of ozone dissociation and exchange at high temperatures using advanced potential energy surfaces and neural network models, successfully reproducing experimental temperature dependencies and providing detailed product state distributions.

Contribution

It introduces a new reproducing kernel-based PES and a neural network model for reaction dynamics, improving the accuracy of temperature dependence predictions and product state distributions.

Findings

QCT simulations reproduce negative temperature dependence of exchange reaction rates.

Both PESs show a 'reef' structure near dissociation affecting temperature dependence.

Neural network models accurately predict product state distributions.

Abstract

The high-temperature atom exchange and dissociation reaction dynamics of the O($^3$P) + O$_2(^3\Sigma_g^{-} )$ system are investigated based on a new reproducing kernel-based representation of high-level multi-reference configuration interaction energies. Quasi-classical trajectory (QCT) simulations find the experimentally measured negative tempe-rature-dependence of the rate for the exchange reaction and describe the experiments within error bars. Similarly, QCT simulations for a recent potential energy surface (PES) at a comparable level of quantum chemical theory reproduce the negative $T-$dependence. Interestingly, both PESs feature a ``reef" structure near dissociation which has been implicated to be responsible for a positive $T-$dependence of the rate inconsistent with experiments. For the dissociation reaction the $T-$dependence correctly captures that known from experiments but underestimates the absolute rates by two orders of magnitude. Accounting for an increased number of accessible electronic states reduces this to one order of magnitude. A neural network-based state-to-distribution model is constructed for both PESs and shows good performance in predicting final translational, vibrational, and rotational product state distributions. Such models are valuable for future and more coarse-grained simulations of reactive hypersonic gas flow.