shows remarkable accuracy for the neutral subset, in fact more accurate than any of the DFT methods studied here. Note that MACE-OFF23 was not trained on charged species, and hence the error is much higher for this subset, but this will be addressed in future models. HF-3c does not perform well here. Thus, machine learning potentials such as these are a reasonable alternative to DFT, particularly for high-throughput, bespoke parametrization work. 88