  Computational catalyst discovery involves the development of microkinetic
reactor models based on estimated parameters determined from density functional
theory (DFT). For complex surface chemistries, the cost of calculating the
adsorption energies by DFT for a large number of reaction intermediates can
become prohibitive. Here, we have identified appropriate descriptors and
machine learning models that can be used to predict part of these adsorption
energies given data on the rest of them. Our investigations also included the
case when the species data used to train the predictive model is of different
size relative to the species the model tries to predict - an extrapolation in
the data space which is typically difficult with regular machine learning
models. We have developed a neural network based predictive model that combines
an established model with the concepts of a convolutional neural network that,
when extrapolating, achieves significant improvement over the previous models.
