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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 3 orders of magnitude. We finally show that Gaussian process corrections serve as informed priors in a Bayesian experimental design framework to learn an accurate a posteriori microkinetic model from few kinetic experiments. We posit that these results suggest that even infusing small, related, high-fidelity thermochemistry data, when available, can systematically and substantially improve the predictive accuracy of microkinetic models. 

Ethylene oxidation by Ag catalysts has been extensively investigated over the past few decades, but many key fundamental issues about this important catalytic system are still unresolved. This overview of the selective oxidation of ethylene to ethylene oxide by Ag catalysts critically examines the experimental and theoretical literature of this complex catalytic system: (i) the surface chemistry of silver catalysts (single crystal, powder/foil, and supported Ag/α-Al2O3), (ii) the role of promoters, (iii) the reaction kinetics, (iv) the reaction mechanism, (v) density functional theory (DFT), and (vi) microkinetic modeling. Only in the past few years have the modern catalysis research tools of in situ/operando spectroscopy and DFT calculations been applied to begin establishing fundamental structure−activity/selectivity relationships. This overview of the ethylene oxidation reaction by Ag catalysts covers what is known and what issues still need to be determined to advance the rational design of this important catalytic system. 

In this work, we propose a strategy to develop data driven local surrogate models of ab initio potential energy functions describing the interaction of adsorbates on heterogeneous catalytic materials. We show that these multivariable surrogate models, based on orthogonal polynomial expansion and trained on sampled ab‐initio energies/forces, can be used to compute harmonic vibrational frequencies and the entropy of adsorbates. Further, we show that the errors in our surrogate model can be estimated and propagated to calculate the uncertainty in the computed properties. We show proof‐of‐concept illustrations of our method to calculate the vibrational frequencies of ethene on 1D edges of molybdenum sulfide (MoS₂), (b) 2D surfaces of Pt(111), and (c) 3D micropores of a HZSM‐5 zeolite; the entropy of ethane adsorbed on Pt(111); and the associated uncertainties in all the cases.