Dissociation cross sections and rates in O 2 + N collisions: molecular dynamics simulations combined with machine learning.

Autor: Huang X; Institute of Atomic and Molecular Physics, Sichuan University, Chengdu 610065, China. chengxl@scu.edu.cn., Gu KM; Institute of Atomic and Molecular Physics, Sichuan University, Chengdu 610065, China. chengxl@scu.edu.cn., Guo CM; Institute of Atomic and Molecular Physics, Sichuan University, Chengdu 610065, China. chengxl@scu.edu.cn., Cheng XL; Institute of Atomic and Molecular Physics, Sichuan University, Chengdu 610065, China. chengxl@scu.edu.cn.; Key Laboratory of High Energy Density Physics and Technology of Ministry of Education, Sichuan University, Chengdu 610065, China.
Jazyk: angličtina
Zdroj: Physical chemistry chemical physics : PCCP [Phys Chem Chem Phys] 2023 Nov 08; Vol. 25 (43), pp. 29475-29485. Date of Electronic Publication: 2023 Nov 08.
DOI: 10.1039/d3cp04044e
Abstrakt: The collision-induced dissociation reaction of O 2 ( v , j ) + N, a fundamental process in nonequilibrium air flows around reentry vehicles, has been studied systematically by applying molecular dynamics simulations on the 2 A ', 4 A ' and 6 A ' potential energy surfaces of NO 2 in a wide temperature range. In particular, we have directly investigated the role of the 6 A ' 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 O 2 + N is dominated by the 6 A ' surface for all except for the low-lying O 2 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 R 2 > 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.
Databáze: MEDLINE