When considering the amount of data required to train an accurate MLP for R1, GAPs both with and without hyperparameter optimisation required ≈ 500 configurations. However, this is surpassed by both ACE and NequIP potentials, where n train ≈ 100 (Fig. ). Comparing training time across these methods is less straightforward due to their different architecture and implementation. In all cases they are found to be efficient and therefore suitable for rapidly developing bespoke MLPs. Among them, NequIP had the greatest training time (half a day using 10 CPU cores + 1 GPU), while GAP and ACE required just 5 ± 2 h of total training time on 10 CPU cores. It is important to note that this difference in training time reduces with the system size, as reference energy and force evaluations dominate the computational time for equally data efficient MLPs.