Unveiling gas-phase oxidative coupling of methane via data analysis

Unveiling the details of the mechanisms of a chemical reaction is a difficult task as reaction mechanisms are strongly coupled with reaction conditions. Here, catalysts informatics combined with high-throughput experimental data is implemented to understand the oxidative coupling of methane (OCM) reaction. In particular, pairwise correlation and data visualization are performed to reveal the relation between reaction conditions and selectivity/conversion. In addition, machine learning is used to fill the gap between experimental data points; thus, a more detailed understanding of the OCM reaction against reaction conditions can be achieved. Therefore, catalysts informatics is proposed for understanding the details of the reaction mechanism, thereby aiding reaction design.

Automated summary

By combining catalysts informatics with high-throughput experimental data, this study explores the oxidative coupling of methane (OCM) reaction mechanism. Pairwise correlation and data visualization are used to uncover the relationship between reaction conditions and selectivity/conversion, with machine learning filling in the gaps between experimental data points to provide a more detailed understanding of the OCM reaction against different reaction conditions. Ultimately, catalysts informatics is proposed as a tool to aid in understanding the intricate details of reaction mechanisms and optimizing reaction design.