Lateral Interactions of Dynamic Adlayer Structures from Artificial Neural Networks

Abstract

Lateral interactions are a key factor in the correct description of adsorption isotherms relevant to heterogeneous catalytic reactions. To model these lateral interactions, a large number of monolayer structures have to be investigated, far exceeding the limitations of conventional techniques such as density functional theory. We have developed a new hybrid neural network model that can substitute the electronic structure calculations for these monolayer structures, without significant loss of accuracy. The low computational cost of this model allows the study of the adlayer structures close to industrial operating conditions. Lateral interactions are found to increase at elevated temperatures as a result of increased adsorbate mobility, and this contribution is found to be key in unifying theoretical and experimental observations. We show that the inclusion of dispersion interactions in stabilizing the adlayers is necessary to obtain correct predictions for both isotherms and adsorption site distributions.