Catalytic Resonance Theory: Parametric Uncertainty in Microkinetic Predictions of Dynamic Rate Enhancement

Microkinetic models are useful tools for screening catalytic materials, however, error in their input parameters can lead to significant uncertainty in model predictions of catalyst performance. Herein, we investigate the impact of linear scaling and Brønsted-Evans-Polanyi relation parametric uncertainty on microkinetic predictions of programmable catalyst performance. Two case studies are considered, a generic A-to-B prototype reaction and the oxygen evolution reaction (OER). Results show that error-unaware models can accurately predict trends and, for the prototype reaction, values of optimal waveform parameters. The specific model parameters driving output uncertainty are identified using a variance-based global sensitivity analysis. However, predictions of dynamic rate enhancement may decrease when uncertainty is propagated into the models. In both cases, operating conditions are identified where the programmable catalyst achieves one-or-more orders of magnitude rate enhancement despite parametric uncertainty in the model, supporting programmable catalysis as a viable strategy for exceeding the Sabatier limit.