A review of the application of Density Functional Theory and machine learning for oxidative coupling of methane reaction for ethylene production

Abstract

The oxidative coupling of methane (OCM) is a reaction with a promise to provide a gainful means of utilizing an abundant greenhouse gas, methane, to produce ethylene; one of the world’s most important chemicals is challenged by the co-production of carbon dioxide, another greenhouse gas. The need to find efficient means of enhancing the reaction with a yield of the desirable C2 product and the reduction in the co-production of COx product continues to be the focus of increased research over the past two decades. The advent of modern computational techniques, including Density Functional Theory (DFT), and data analytical techniques, such as Machine Learning (ML), have inspired new ways of generating data and drawing intuition on the ways to improve the efficacy of the OCM reaction. This study focuses on highlighting the innovations carried out in the study of the OCM reaction over the last 22 years: the reaction mechanism, kinetics, and catalytic design. Despite the concerted efforts to model and design new catalysts, the development of improved catalysts that are selective for C2 yields higher than 30% at low temperatures continues to be a bottleneck in the process. The application of ML and DFT in OCM is poised to provide a means to predict, design, and develop new catalysts that will enhance the effectiveness of the reaction and the quality of the products. Both techniques provide opportunities to improve and ameliorate challenges bedeviling the OCM reaction, including the high activation energy, low C2 yield, and catalyst instability/deactivation.