Machine-Learning Analysis and Discovery of Heterogeneous Catalysts Effective for Oxidative Coupling of Methane

Abstract

Catalytic performance on oxidative coupling of methane (OCM) reaction was predicted by using two kinds of machine learning (ML) approaches using previously-reported experimental data. The first approach considers catalyst compositions and experimental condition as input value. The second approach considers elemental features as input representations instead of inputting catalyst compositions directly. Among 10-fold cross validation , XGB Regressor provided the best results, and prediction accuracy was improved by the second approach. In addition, SHAP values were calculated to evaluate the most influenced input variables on catalyst performance. Experimental conditions such as reaction temperature and partial pressure of reaction gases, as well as catalyst compositions such as Mn, Na, and Li were identified to be highly important. Partial dependence plot was obtained to visualize the relationship between catalytic performance and catalyst composition on the Mn/Na ₂ WO ₄ /SiO ₂ type catalyst. Finally, optimization of catalyst composition and experimental condition were explored using a SMAC procedure with ML as a “surrogate model”. Top 20 promising candidate catalysts were identified for future study.