  Universal potentials open the door for DFT level calculations at a fraction
of their cost. We find that for application to systems outside the scope of its
training data, CHGNet\cite{deng2023chgnet} has the potential to succeed out of
the box, but can also fail significantly in predicting the ground state
configuration. We demonstrate that via fine-tuning or a $\Delta$-learning
approach it is possible to augment the overall performance of universal
potentials for specific cluster and surface systems. We utilize this to
investigate and explain experimentally observed defects in the Ag(111)-O
surface reconstruction and explain the mechanics behind its formation.
