Predicting Catalytic Activity for CH4 Combustion on Pd-exchanged Zeolite Catalysts Using Automated Reaction Route Mapping

To address CH4 emissions, one of the major contributors to global warming, developing efficient CH4 combustion catalysts is crucial. While computational predictions of catalytic activity are highly desired, conventional methods struggle with the complexity of reactions on solid catalyst surfaces. We explored reaction mechanisms of CH4 combustion on Pd-exchanged zeolites (Pd-CHA, Pd-beta, and Pd-MOR) by combining neural network potential (NNP) with automated reaction route mapping. The predicted reaction map of CH4 combustion over Pd2+ site revealed partially oxidized species such as CH2O, HCOOH, and bicarbonate as potential intermediates toward CO2 + 2H2O. Activation energies (Ea) of the rate-determining steps (RDS) were evaluated, revealing the order of Ea as Pd-MOR < Pd-beta < Pd-CHA. A kinetic analysis using rate constant matrix contraction (RCMC) method estimated the catalytic activity of these catalysts. Pd-beta and Pd-MOR exhibited no intermediates with significant lifetimes, whereas Pd-CHA showed stable bicarbonate intermediates, decreasing the formation rate of CO2 + 2H2O. Kinetic analysis further predicted the pseudo CO2 formation rate with activity order Pd-MOR > Pd-beta > Pd-CHA, aligning with experimental results. These findings demonstrate the potential of the automated reaction route mapping with NNP for predicting zeolite catalyst activity, enabling pre-screening and rational design of solid catalysts.