Machine Learning Study of Methane Activation by Gas-Phase Species.

Abstract

The activation and transformation of methane have long posed significant challenges in scientific research. The quest for highly active species and a profound understanding of the mechanisms of methane activation are pivotal for the rational design of related catalysts. In this study, by assembling a data set encompassing a total of 134 gas-phase metal species documented in the literature for methane activation via the mechanism of oxidative addition, machine learning (ML) models based on the backpropagation artificial neural network algorithm have been established with a range of intrinsic electronic properties of these species as features and the experimental rate constants of the reactions with methane as the target variables. It turned out that the satisfactory ML models could be described in terms of four key features, including the vertical electron detachment energy (VDE), the absolute value of the energy gap between the highest occupied molecular orbital of CH₄, and the lowest unoccupied molecular orbital of the metal species (|ΔE_H'-L|), the maximum natural charge of metal atoms (Q_max), and the maximum electron occupancy of valence s orbitals on metal atoms (n_{s_max}), based on the feature selection complemented with manual intervention. The stability and generalization ability of the constructed model was validated using a specially designed data-splitting strategy and newly incorporated data. This study proved the feasibility and discussed the limitations of the ML model, which is described by four key features to predict the reactivity of metal-containing species toward methane through oxidative addition mechanisms. Furthermore, a careful preparation of the training data set that covers the full expected range of target and feature values aiming to achieve good predictive accuracy is suggested as a practical guideline for future research.