We then turned our attention to the recently developed Atomic Cluster Expansion (ACE ) and Neural Equivariant Interatomic Potentials (NequIP ) MLP methods and evaluated their performance within the same training strategy for R1 (Fig. ). While rather different in philosophy, both ACE and NequIP outperform GAPs and provide MLPs of similar accuracy (Fig. , Fig. ). Here, accuracy is based on deviations between QM ground-truth energies (true) and predicted energies over independent DFT-MD trajectories propagated from the TS to the reactant and product states. Previously, we have shown that a prospective validation strategy in the configuration space accessible to that MLP is essential to characterising 'good' MLPs. These potentials are stable by construction as we use an AL strategy (Fig. , bottom left), where we define a stable potential where 10 × 1 ps trajectories can be propagated without encountering a configuration that deviates > 2.3 kcal mol -1 (0.1 eV) from the true energy.