  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. 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.
