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The temperature dependence of the thermal rate constant for the reactionCl($^2$P) + CH$_4$ $\rightarrow$ CH$_3$ + HCl is calculated using a GaussianProcess machine learning (ML) approach to train on and predict thermal rateconstants over a large temperature range. Following procedures developed in twoprevious reports, we use a training dataset of approximately 40reaction/potential surface combinations, each of which is used to calculate thecorresponding data base of rate constant at approximately eight temperatures.For the current application, we train on the entire dataset and then predictthe temperature dependence of the title reaction employing a "split" datasetfor correction at low and high temperatures to capture both tunneling andrecrossing. The results are an improvement on recent RPMD calculations comparedto accurate quantum ones, using the same high-level ab initio potential energysurface. Both tunneling at low temperatures and recrossing at high temperaturesare observed to influence the rate constants. Recrossing effects, which are notdescribed by TST and even sophisticated tunneling corrections, do appear inexperiment at temperatures above around 600 K. The ML results describe theseeffects and in fact, merge at 600 K with RPMD results (which can describerecrossing), and both are close to experiment at the highest experimentaltemperatures.

A Machine Learning Approach for Rate Constants III: Application to the Cl($^2$P) + CH$_4$ $\rightarrow$ CH$_3$ + HCl Reaction