Issue 20, 2023, Issue in Progress

Full-dimensional neural network potential energy surface and dynamics of the CH2OO + H2O reaction

Abstract

An accurate global full-dimensional machine learning-based potential energy surface (PES) of the simplest Criegee intermediate (CH2OO) reaction with water monomer was developed based on the high level of extensive CCSD(T)-F12a/aug-cc-pVTZ calculations. This analytical global PES not only covers the regions of reactants to hydroxymethyl hydroperoxide (HMHP) intermediates, but also different end product channels, which facilities both the reliable and efficient kinetics and dynamics calculations. The rate coefficients calculated by the transition state theory with the interface to the full-dimensional PES agree well with the experimental results, indicating the accuracy of the current PES. Extensive quasi-classical trajectory (QCT) calculations were performed both from the bimolecular reaction CH2OO + H2O and from HMHP intermediate on the new PES. The product branching ratios of hydroxymethoxy radical (HOCH2O, HMO) + OH radical, formaldehyde (CH2O) + H2O2 and formic acid (HCOOH) + H2O were calculated. The reaction yields dominantly HMO + OH, because of the barrierless pathway from HMHP to this channel. The computed dynamical results for this product channel show the total available energy was deposited into the internal rovibrational excitation of HMO, and the energy release in OH and translational energy is limited. The large amount of OH radical found in the current study implies that the CH2OO + H2O reaction can provide crucially OH yield in Earth's atmosphere.

Graphical abstract: Full-dimensional neural network potential energy surface and dynamics of the CH2OO + H2O reaction

Article information

Article type
Paper
Submitted
30 Mar 2023
Accepted
16 Apr 2023
First published
02 May 2023
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2023,13, 13397-13404

Full-dimensional neural network potential energy surface and dynamics of the CH2OO + H2O reaction

H. Wu, Y. Fu, W. Dong, B. Fu and D. H. Zhang, RSC Adv., 2023, 13, 13397 DOI: 10.1039/D3RA02069J

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