Neural network predictions of oxygen interactions on a dynamic Pd surface

Artificial neural networks (NNs) are increasingly common in quantum chemistry applications. These models can be trained to higher-level ab-initio calculations and are capable of achieving arbitrary levels of accuracy. The most common applications thus far have been specialised for either bulk or surface structures of up to two chemical components. However, very few of these studies utilise NNs trained to high-dimensional potential energy surfaces, and there are even fewer studies which examine adsorbate-adsorbate and adsorbate-surface interactions with those NNs. The goal of this work is to determine the feasibility of and develop methodologies for producing a high-dimensional NN capable of reproducing coverage-dependent oxygen interactions with a dynamic Pd fcc(111) surface. We utilise the atomistic machine-learning potential software package to generate a Behler-Parrinello local symmetry function NN trained on a large database of density functional theory (DFT) calculations. These training methods are flexible, and thus easily expanded upon as demonstrated in previous work. This allows the database of high quality PdO DFT calculations to be used as a basis for future work, such as the inclusion of a third chemical species, for example a binary Pd alloy, or another adsorbate atom such as hydrogen.