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

While computational predictions of catalytic activity are highly desired, conventional methods have difficulty capturing the complexity of reactions on solid catalyst surfaces. To address this issue, we employed a novel approach combining neural network potentials (NNPs) with automated reaction route mapping to explore reaction mechanisms of CH4 combustion on Pd-exchanged zeolites (Pd-CHA, Pd-beta, and Pd-MOR). The predicted reaction map of CH4 combustion over Pd2+ site revealed partially oxidized species such as CH2O, HCOOH, and bicarbonate as the potential intermediates toward CO2 + 2H2O. Activation energies (Ea) of the rate-determining step (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 activities of these catalysts. No reaction intermediates with significant lifetimes were observed on Pd-beta and Pd-MOR, whereas stable bicarbonate intermediates were present on Pd-CHA, 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 the catalytic activity of solid catalysts, enabling their efficient pre-screening and rational design.