The highly exothermic reaction H(2S) + O3(X1A1) → OH(X2Π) + O2(X3Σg-) reaction plays an important role in the upper atmosphere, which is known to be responsible for Meinel overtone bands of the hydroxyl radical in the airglow. A machine learning method is used to construct a high-fidelity multichannel global reactive potential energy surface (PES) for the HO3 system from 21452 high-level ab initio calculations at MRCI-F12/VDZ-F12 level of theory. The permutation invariance of the PES with respect to the three identical oxygen atoms is enforced using permutation invariant polynomials (PIPs) in the input layer of a neural network (NN). Thermal rate coefficients have been calculated based on this accurate PIP-NN PES, using ring polymer molecular dynamics (RPMD) and quasi-classical trajectory (QCT). The QCT rate coefficients are in good agreement with the experiment at high temperatures, but is significantly lower than the experimental values at low temperatures. The RPMD approach yielded results that are in generally better agreement with experimental rate coefficients than QCT ones, especially at low temperatures, attributable to its capacity to capture quantum effects such as tunneling and zero-point energy. Meanwhile, the RPMD rate coefficients are used to assess the available experimental data, which are quite scattered and contain large uncertainties, and support one group of existing experiments over the other. The OH vibrational distribution is also analyzed based on the final state information of QCT, which agrees better with experiments than previous QCT results based on DMBE I PES. Our study shows that the released energy mainly flows into the vibration of the OH radical in the whole temperature range, which reveals the detailed dynamic mechanism of OH high vibrational excitation. In addtion, a fancy dynamic phenomenon were observed in the O + HO2 reaction. These studies shed valuable light on the atmospheric chemistry.

Theoretical studies on dynamics of atmospheric chemical reactions in HO3 system

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