Up to this point, we have used classification and regression to focus on only one step in the ORR cycle (O2 binding) as previous studies have indicated binding oxygen to the catalyst is involved in limiting the rate. In order to connect these methods more directly to the process of discovering new N + -GCC molecules that are ORR active, we applied this approach to search directly for the molecules in this dataset with the smallest ORR overpotential predicted by DFT (see Methods section for details). The MLP regressor using the same architecture as before achieved an MAE of 0.085 ± 0.021 on predicting overpotential for each catalyst when trained with all features, and 0.123 ± 0.014 when trained with only structural features. Analogous to the previous scenario, we conducted a simulated search within our dataset, with two differences: the target property is overpotential rather than O2 binding, and we adjusted the target search space to the top 50 catalysts (Figure ).