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

TL;DR

This paper employs a Gaussian Process machine learning approach to accurately predict the temperature-dependent rate constants of the Cl($^2$P) + CH$_4$ reaction across a broad temperature range, capturing tunneling and recrossing effects.

Contribution

It introduces a novel ML-based method trained on multiple datasets to improve rate constant predictions, including effects beyond traditional models.

Findings

ML predictions align well with experimental data at high temperatures.

The approach captures tunneling effects at low temperatures.

Recrossing effects are accurately modeled at high temperatures.

Abstract

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.