Data-Driven Identification of the Reaction Network in Oxidative Coupling of the Methane Reaction via Experimental Data

Identifying details of chemical reactions is a challenging matter for both experiments and computations. Here, the reaction pathway in oxidative coupling of methane (OCM) is investigated using a series of experimental data and data science techniques in which data are analyzed using a variety of visualization techniques. Data visualization, pairwise correlation, and machine learning unveil the relationships between experimental conditions and the selectivities of CO, CO2, C2H4, C2H6, and H2 in the OCM reaction. More importantly, the reaction network for the OCM reaction is constructed on the basis of the scores provided by machine learning and experimental data. In particular, the proposed reaction map not only contains the chemical compound but also contains experimental conditions. Thus, data-driven identification of chemical reactions can be achieved in principle via a series of experimental data, leading to more efficient experimental design and catalyst development.