Abstract
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.
Supplementary materials
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Supporting Information
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The result of Bader charge analysis and comparison to the result from VASP
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