Dissociation cross sections and rates in O2 + N collisions: molecular dynamics simulations combined with machine learning.

The collision-induced dissociation reaction of O2 (v, j) + N, a fundamental process in nonequilibrium air flows around reentry vehicles, has been studied systematically by applying molecular dynamics simulations on the 2A', 4A' and 6A' potential energy surfaces of NO2 in a wide temperature range. In particular, we have directly investigated the role of the 6A' surface in this process and discussed the applicability of the simplified approximate rate models proposed by Esposito et al. and Andrienko et al. based on the lowest two surfaces. The present work indicates that the state-selected dissociation of O2 + N is dominated by the 6A' surface for all except for the low-lying O2 states. Furthermore, a complete database of rovibrationally detailed cross sections and rate coefficients is a prerequisite for modeling the relevant nonequilibrium air flows in spacecraft reentry. Here, the combination of the quasi-classical trajectory (QCT) and the neural network (NN) has been proposed to predict all state-selected dissociation cross sections and further construct dissociation parameter sets. All NN-based models established in this work accurately reproduce the results calculated from QCT simulations over a wide range of rovibrational quantum numbers with R2 > 0.99. Compared with the explicit QCT simulations, the computational requirement for predicting cross sections and rates based on the NN models significantly reduces. Finally, thermal equilibrium rate coefficients computed from NN models match remarkably well the available theoretical and experimental results in the whole temperature range explored.

Exhaustive state-specific dissociation study of the N2(Σg+1)+N(S4) system using QCT combined with a neural network method.

This work studies the exhaustive rovibrational state-specific collision-induced dissociation properties of the N2+N system by QCT (quasi-classical trajectory) combined with a neural network method based on the ab initio PES recently published by Varga et al. [Phys. Chem. Chem. Phys. 23, 26273 (2021)]. The QCT combined with a neural network for state-specific dissociation (QCT-NN-SSD) model is developed and used to predict the dissociation cross sections and their energy dependence on the thermal range from a sparsely sampled noisy dataset. It is conservatively estimated that using this method can reduce the cost of the calculation by 96.5%. The rate coefficient of thermal non-equilibrium between different energy modes is obtained by combining the QCT-NN-SSD model with the multi-temperature model. The results show that, for the equilibrium state, dissociation mainly occurs at high vibrational and moderately low rotational levels. When the system is in non-equilibrium, there is no obvious vibrational level preference and highly rotationally excited molecules play a major role in promoting the dissociation by compensating for the lack of vibrational energy. The use of neural network training to generate complete datasets based on limited and discrete data provides an economical and reliable way to obtain a complete kinetic database needed to accurately simulate non-equilibrium flows.