Exploring Structure-Sensitive Relations for Small Species Adsorption Using Machine Learning

Accurate prediction of adsorption
energies on heterogeneous catalyst
surfaces is crucial to predicting reactivity and screening materials.
Adsorption linear scaling relations have been developed extensively
but often lack accuracy and apply to one adsorbate and a single binding
site type at a time. These facts undermine their ability to predict
structure sensitivity and optimal catalyst structure. Using machine
learning on nearly 300 density functional theory calculations, we
demonstrate that generalized coordination number scaling relations
hold well for oxygen- and high-valency carbon-binding species but
fail for others. We reveal that the valency and the electronic coupling
of a species with the surface, along with the site type and its coordination
environment, are critical for small species adsorption. The model
simultaneously predicts the adsorption energy and preferred site and
significantly outperforms linear scalings in accuracy. It can expose
the structure sensitivity of chemical reactions and enable enhanced
catalyst activity via engineering particle shape and facet defects.
The generality of our methodology is validated by training the model
with transition metal data and transferring it to predict adsorption
energies on single-atom alloys.