For the ORR reaction, one of the most relevant classification questions is to answer, "is this atom capable of binding O2?" We trained several classifiers using different decision functions on the features collected from the catalyst dataset. In each case, we trained the machine learning model to the DFT-predicted O2-binding capability for each of the 4039 sites in our data set, using only descriptors from the unbound structure. The accuracy of each model is shown in Table . To visually evaluate the classifiers, we projected the decision boundaries of the classifiers onto the two continuous features most strongly correlated with activity, which are spin density and charge difference (Figure ). Figure shows that a high spin density on a carbon combined with a large negative change in the CHELPG 57 partial charge difference leads to a strong probability of activity.