Increasing Ethene Yield via Oxidative Coupling of Methane at Low Temperature: An Application of Machine Learning and DFT in the Design and Innovation of Effective Catalyst Compositions

The combination of catalytic, electronic properties and experimental data provides more information for the analysis of catalysts to improve innovation and design. Here, we compute electronic properties, including the bandgap, fermi energy and magnetic moment of known catalysts of the oxidative coupling of methane reaction (OCM). In combination with available data on experimental conditions for OCM, we are able predict catalytic performance and reaction outcomes in the form of methane yield, ethene yield, ethane yield, and carbon dioxide yield. A comparison of different machine learning models suggests Extreme Gradient Boost Regression (Xgboost Regression) is an ideal model for predicting catalytic performance with great accuracy. The fermi energy of the catalyst promoter, its atomic number, and the active metal oxide band gap have been found to be good electronic descriptors of the catalytic performance of the OCM reaction. Transition metals, including Platinium, Rhodium, Ruthenium and Iridium, have been predicted to promote catalyst performance in the oxidative coupling of methane reaction. The study proposes seventy-nine novel bimetallic combinations for metal dioxides and 616 novel catalytic materials for methane conversion at a low temperature of 700°C as an effective catalyst for oxidative coupling of methane reaction. These new catalysts were predicted to enhance methane yield in the range of +/-30% to +/-95%, an increase from the prior research's maximum methane conversion of 36.