Having successfully trained this ACE MLP for R2, we propagated dynamics to compute product distribution. Using 100 trajectories, each of them taking only minutes, we were able to obtain converged results (Fig. ). This efficiency also allowed us to propagate ring polymer MD (RPMD) using the developed ACE potential. As expected, given the nature of the bonds being formed during this reaction, the product ratios were similar to those obtained using classical MD. We also evaluated the effect of temperature by equilibrating the system at the TS using constrained NVT dynamics (1 eV Å -2 , ≈ 0.5 kcal mol -1 additional ZPE), prior to propagating NVE downhill dynamics. Both direct NVE downhill and equilibrated dynamics provide qualitatively similar trends. However, a more detailed analysis shows that the latter affords almost double the proportion of R2 P1 and the formation of R2 P3 as 1% of the total product distribution, which would otherwise not be observed (right most distribution in Fig. ). This demonstrates the importance of propagating equilibrated quantum dynamics to obtain converged distributions. Training an MLP uniquely allows for the 100-fold more expensive simulations to be performed, with the effect of ZPE and tunnelling accounted for.