Accessing the usefulness of atomic adsorption configurations in predicting the adsorption properties of molecules with machine learning

We study the effect of adding atomic adsorption configurations into the training and validation dataset for a neural network's predictions of the adsorption energies of small molecules on single metal and bimetallic, single crystal surfaces.