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

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 number
of reaction intermediates can be very large, and the cost of calculating
the adsorption energies by DFT for all surface intermediates even
for one active site model can become prohibitive. In this paper, we
have identified appropriate descriptors and machine learning models
that can be used to predict a significant part of these adsorption
energies given data on the rest of them. Moreover, our investigations
also included the case when the species data used to train the predictive
model are of different size relative to the species the model tries
to predictthis is an extrapolation in the data space which
is typically difficult with regular machine learning models. Due to
the relative size of the available data sets, we have attempted to
extrapolate from the larger species to the smaller ones in the current
work. Here, we have developed a neural network based predictive model
that combines an established additive atomic contribution based model
with the concepts of a convolutional neural network that, when extrapolating,
achieves a statistically significant improvement over the previous
models.