About this scholarly article

A Machine Learning Approach for Rate Constants III: Application to the Cl($^2$P) + CH$ 4$ $\rightarrow$ CH$ 3$ + HCl Reaction by Houston, Paul L.; Nandi, Apurba; Bowman, Joel M. is a scholarly article available to read on EtoBox.

The temperature dependence of the thermal rate constant for the reaction Cl($^2$P) + CH$_4$ $\rightarrow$ CH$_3$ + HCl is calculated using a Gaussian Process machine learning (ML) approach to train on and predict thermal rate constants over a large temperature range. Following procedures developed in two previous reports, we use a training dataset of approximately 40 reaction/potential surface combinations, each of which is used to calculate the corresponding data base of rate constant at approximately eight temperatures. For the current application, we train on the entire dataset and then predict the temperature dependence of the title reaction employing a "split" dataset for correction at low and high temperatures to capture both tunneling and recrossing. The results are an improvement on recent RPMD calculations compared to accurate quantum ones, using the same high-level ab initio potential energy surface. Both tunneling at low temperatures and recrossing at high temperatures are observed to influence the rate constants. Recrossing effects, which are not described by TST and even sophisticated tunneling corrections, do appear in experiment at temperatures above around 600 K. Th

Author

Houston, Paul L.; Nandi, Apurba; Bowman, Joel M.

Published

2022

Language

EN