Predicting the selectivity and reactivity of novel catalysts at industrially relevant conditions requires a detailed microkinetic mechanism, comprising many elementary reactions. Recent advances use a combination of scaling relations, machine learning, and DFT calculations, to gradually refine a microkinetic model until the rate limiting steps have been calculated with sufficient accuracy to be confident that they are correctly identified. However, such a system requires as input a comprehensive kinetic model containing all the possible pathways. Our recently developed Reaction Mechanism Generator for Heterogeneous Catalysis (RMG-Cat) [1], built upon the open-source RMG software primarily used for gas-phase pyrolysis and combustion [2], can provide such mechanisms ab inito: the user supplies just the initial conditions (eg. reactant composition, temperature, pressure) and the software predicts all the possible reactions, estimates the thermochemical and kinetic parameters, solves the governing equations, and decides which reaction pathways to include and explore further. RMG-Cat makes its decisions regarding which pathways to explore and which to ignore, using the estimated parameters, so it is important that the estimates are reasonable, even if the important parameters will be refined with more accurate calculations later in the model development process. Linear Scaling Relations (LSRs) can provide reasonable estimates of adsorption energies in a very computationally efficient manner. We have now implemented linear scaling relationships for the estimation of adsorption energies in the RMG-Cat software. Our database of parameters is organized in a hierarchical tree structure, enabling detailed functional group descriptions to be used when data are available and more general descriptions to be used when necessary. We include new parameters to describe many adsorbates on a range of metal surfaces, and a framework to re-train the parameters whenever new data are available.<br/><br/>

Automatic microkinetic mechanism generation for heterogeneous catalysis

256th National Meeting (2018)

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