Machine Learning Study of Methane Activation by Gas-Phase Species

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 (|ΔEH′–L|), the maximum natural charge of metal atoms (Qmax), and the maximum electron occupancy of valence s
orbitals on metal atoms (ns_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.