In order to elucidate design principles for developing N + -GCC ORR catalysts, we analyzed the impact of the input data on the classifier accuracy using permutation feature importance (Figure ). This analysis identified Partial Charge Difference, the change in CHELPG charge at a carbon atom when the molecule is ionized, and Spin Density, the Mulliken spin population at the active site, as the two most important features for predicting whether a particular carbon will bind O2. The ionization energy also appears to have a significant contribution, and between the impact of these three features (none of which are derived directly from the structure) it is clear why electronic features improved the model accuracy from 87% to 95%. Each of these features also reflects some aspect of the process of drawing electrons off of the molecule. In this dataset, the carbons most likely to be active are the ones with a high spin density and a large change in charge density after ionization, and which are in molecules with a low ionization energy. Overall, these features identify which atoms can most stably donate electron density to O2 during bond formation. Although electronic features are the three most important features identified by this analysis, the next five most important features are all derived directly from the structure. The Ortho or Para feature, which indicates whether an active site is ortho or para to a nitrogen atom in an aromatic ring, and the Distance to Nitrogen feature both demonstrate that the pyridinic moieties have a significant local impact on catalytic activity. For the Nearest Nitrogen Distance feature, Figure shows that carbon atoms an odd number of bonds away from a nitrogen are more likely to be active. This can be thought of as ortho-para activation to O2 binding generalized to polyaromatic systems. Carbons that are three bonds away from a nitrogen, even if they are technically not para because they are in an adjoining aromatic ring, are still activated.