The overall process of developing the dataset, generating features, and applying machine learning models to predict O2-binding is shown in Figure . This approach enables us to quantitatively analyze the effect of both structural and electronic properties of the catalysts, providing statistically motivated insights on which carbon atoms are likely to be catalytically active for any given N + -GCCs. We find that carbon atoms that are likely active sites are an odd number of bonds from a nitrogen atom, are part of only a single aromatic ring, and accumulate spin density when an excess electron is present on the catalyst. Further, such carbons are most likely to be active if they are part of a catalyst that can be more easily ionized, and if the change in partial charge at that site changes significantly between the neutral and cationic state of the catalyst. Although the electronic features explored in this work are the most important predictors of activity, the HTVS scenarios we explore show that models trained on structural features alone can still perform well for certain goals.