Design of low temperature La2O3 oxidative coupling of methane catalysts using feature engineering and automated sampling

Abstract

The design of efficient catalysts remains an challenge for complex systems such as the oxidative coupling of methane (OCM), where reaction mechanisms are still debated. Catalysts informatics workflows have proved useful in identifying high-performing material candidates and optimizing reaction conditions from underlying trends in experimental data. Herein, a data set composed La₂O₃-supported catalysts for the OCM reaction is used to construct a support-vector regression (SVR) model and extract four element combinations to support on La₂O₃ and test for low temperature catalytic activity, with the best result observed for (Y, Cs)/La₂O₃. This methodology presents an effective approach from building a regression model using engineered features with an automated sampling technique to the extraction and experimental validation of promising catalyst candidates, which can also be extended toward other catalytic reactions.