A Machine Learning Approach for Rate Constants. III. Application to the Cl(2P) + CH4 → CH3 + HCl Reaction.

Abstract

The temperature dependence of the thermal rate constant for the reaction Cl(3P) + CH4 → HCl + CH3 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 data set of approximately 40 reaction/potential surface combinations, each of which is used to calculate the corresponding database of rate constant at approximately eight temperatures. For the current application, we train on the entire data set and then predict the temperature dependence of the title reaction employing a "split" data set 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 significant recrossing at high temperatures are observed to influence the rate constants. The recrossing effects, which are not described by TST and even sophisticated tunneling corrections, do appear in experiment at temperatures above around 600 K. The ML results describe these effects and in fact merge at 600 K with RPMD results (which can describe recrossing), and both are close to experiment at the highest experimental temperatures. These results are in accord with a recent high-level experiment-theory study of this reaction.