Accelerating active catalyst discovery: a probabilistic prediction-based screening methodology with applications in dry reforming of methane

Abstract

Dry reforming of methane (DRM) is a promising technology for syngas production from CH4 and CO2. However, discovering feasible and efficient catalysts remains challenging despite recent advancements in machine learning. Herein, we present a novel probabilistic prediction-based, high-throughput screening methodology that demonstrates outstanding performance, with a coefficient of determination (R2) of 0.936 and root-mean-square error (RMSE) of 6.66. Additionally, experimental validation was performed using 20 distinct catalysts to ensure the accurate verification of the model, 17 of which were previously unreported combinations. Our model accurately predicts CH4 conversion rates and probability values by considering catalyst design, pretreatment, and operating variables, providing reliable insights into catalyst performance. The proposed probabilistic prediction-based screening methodology, which we introduce for the first time in the field of catalysis, holds significant potential for accelerating the discovery of catalysts for DRM reactions and expanding their application scope in other crucial industrial processes. Thus, the methodology effectively addresses a key challenge in the development of active catalysts for energy and environmental research.