Leveraging Thermochemistry Data to Build Accurate Microkinetic Models

Abstract

Ab initio microkinetic modeling, parameterized using density functional theory (DFT) energies, is a common tool to quantify reaction rates and analyze reaction mechanisms a priori in heterogeneous catalysis. Such models, however, often have large prediction errors even if they include plausible reaction steps and correctly model the active sites; this is partially due to the intrinsic inaccuracies of the chosen DFT functional. Borrowing concepts from Bayesian calibration theory, we show that transferable data-driven corrections to DFT energies in the form of Gaussian process models trained on single crystal adsorption calorimetry data can improve the accuracy of microkinetic models substantially. Specifically, we demonstrate that such corrections improve the predictive accuracy of the microkinetic model of the water gas shift reaction on single crystal Cu(111) surface by three (3) orders of magnitude. We finally show that Gaussian Process corrections serve as informed priors in a Bayesian experimental des...