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

TL;DR

This paper presents a neural network model that combines convolutional techniques to accurately predict adsorption energies on metal surfaces, enabling efficient catalyst discovery by overcoming DFT computational costs.

Contribution

It introduces a novel multiple filter neural network that improves extrapolation of adsorption energies, addressing data size variability and enhancing predictive accuracy.

Findings

Significant improvement in extrapolation accuracy over previous models

Effective prediction of adsorption energies with limited or varying data

Enhanced catalyst discovery process through reduced computational costs

Abstract

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.