Analysis of the Descriptors for the Oxidative Coupling of Methane Reaction, Using Varying Machine Learning Approaches †

The fusion of catalytic and electronic properties, coupled with empirical data, provides enriched perspectives into catalyst evaluation and design, thus propelling advancement and innovation in the domain of heterogeneous catalytic reactions, including the oxidative coupling of methane (OCM) reaction. Comparative assessment of various machine learning methodologies on OCM reaction datasets reveals that the Random Forest regression (RFR) model excels in C₂H₄ and C₂H₆ combined yield (C₂y) predictive accuracy, boasting an average R² value of 0.98. The hierarchy of modeling performance stands as follows: RFR > XGBR > SVR > DNN. The MSE and MAE metrics of the RFR models were observed to be lower compared to alternative models, ranging from 0.12 to 9.03 for MSE and 0.21 to 2.02 for MAE. Model accuracy follows the order of C₂H₆y > C₂H₄y > C₂y > CO₂y > CH₄_conv (methane conversion). When examining the influence of model features, C₂y increases proportionally with an augmentation in dataset attributes, including the quantity of alkali/alkali-earth metal moles in the catalyst (13.69%), the atomic number (6.24%) of the catalyst promoter, and the Fermi energy of the metal, with a less pronounced impact compared to the case of temperature (33.70%). This suggests a highly nonlinear correlation between combined ethylene and ethane yield and temperature. Other factors, such as the bandgap of the active metal oxide and the support, as well as the Fermi energy of the catalyst support, were observed to have a relatively modest effect on the predictive models for combined ethylene and ethane yield and methane conversion.