In Figure we compare the energetics of our rMLP-calculated barriers to our validation set of 23 Pynta-DFT barriers. We generally observe that the pre-refined energy barriers (green) are slightly higher than the Pynta/DFT (red) ground truth. Indeed, the meanabsolute errors (MAE) reduce from 0.17 eV (pre-refinement) to 0.03 eV after Stage 3 refinement across the 23 validation reactions. Importantly, both our pre-and post-refinement MAE value is lower than the 0.23 eV value obtained from previous BEP relationships for these reactions. Before proceeding further, it is useful to discuss the computational cost associated with this approach. Overall we obtain an 7x speedup compared to base-Pynta for the set of 53 reactions. A complete accounting of the computational expense is provided in the SI (Table-S5). A benefit of our model is that fast inference times (on the order of ms) enable the Pynta workflow to be completed in just a few hours and can allow the model to be used for subsequent dynamic studies. Also, despite the relatively large size of our DFT dataset, we are able to train our model in less than 8 hours on a single CPU-node on the NERSC Perlmutter machine (2x AMD EPYC 7763). Achieving DFT-accuracy for transition states with this level of speedup facilitates larger-scale studies than were previously feasible due to computational constraints. We note that even greater speedups are possible and we will discuss potential improvements to this workflow in subsequent sections.