Of the ML methods we evaluate to classify carbons as active or inactive with respect to binding O2, our most effective method (which used both electronic and structural features of the catalysts) achieves 95.3% accuracy. We also train a regression model to predict O2 binding energy, which achieves an MAE of 0.077 eV with an R 2 value of 0.70. Permutation importance indicates that the two most important predictors of O2-binding activity are the site's spin density and its change in charge at that site when the catalyst is oxidized. These features are likely important predictors because during the O2 binding process, electron density moves from the catalyst onto the terminal oxygen atom. Several structural features are also important to a lesser degree; these features broadly reflect the energy required to convert the carbon from sp 2 to sp 3 hybridization.