To circumvent this challenge, we explored a reaction-class-based training strategy that categorized the 53 reactions into multiple distinct groups based on the types of bonds being broken. This categorization resulted in seven different models (N.B.: not to be confused with the seven previous model iterations), each corresponding to a specific category of transition state (see Figure ). Each model was trained with the base dataset from the DP 0 model, along with all data generated from Pynta for each reaction class over the 7 concurrent learning iterations from the total model approach. Compared to the total model approach discussed above, the reaction-class strategy successfully identified transition states (TS) for all 53 reactions, both before and after DFT refinement. As shown in Figure , the mean absolute error (MAE) for pre-refinement TSs was 0.11 eV, which improved to 0.03 eV postrefinement. Additionally, the reaction class models exhibited lower mean absolute errors on force predictions when compared to the total model, even after hyperparameter tuning of the latter. We gain additional insights when investigating the reactions which failed DFT-refinement in our total approach but were successful in the reaction class approach. As shown in Figure , the reaction class approach obtained lower model uncertainties and DFT-residual forces compared to the total model approach in most cases. As this reaction class approach provides a comprehensive exploration of all transition state geometries, we now compare the results of this automated workflow with previous studies in the literature. Specifically, previous work by Aljama et al. has reported the free energy diagram of the minimum energy pathway (MEP) connecting methanol (i.e., the reactant) to the desired product (i.e., formaldehyde.) Figure compares these literature results with our MEP obtained using the DFT-refined transition states from our reaction class model.