Augmentation of Universal Potentials for Broad Applications
Joe Pitfield, Florian Brix, Zeyuan Tang, Andreas Møller Slavensky, Nikolaj Rønne, Mads-Peter Verner Christiansen, Bjørk Hammer
Abstract
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, pretrained CHGNet [Deng et al., Nat. Mach. Intell. 5, 1031 (2023)] 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 Δ-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 their formation.