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Computational catalyst discovery involves the development of microkineticreactor models based on estimated parameters determined from density functionaltheory (DFT). For complex surface chemistries, the cost of calculating theadsorption energies by DFT for a large number of reaction intermediates canbecome prohibitive. Here, we have identified appropriate descriptors andmachine learning models that can be used to predict part of these adsorptionenergies given data on the rest of them. Our investigations also included thecase when the species data used to train the predictive model is of differentsize relative to the species the model tries to predict - an extrapolation inthe data space which is typically difficult with regular machine learningmodels. We have developed a neural network based predictive model that combinesan established model with the concepts of a convolutional neural network that,when extrapolating, achieves significant improvement over the previous models.

A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications