Although generating quantum chemistry features can be useful for developing a conceptual understanding of ORR for these catalysts, it is important to address whether the compute time of DFT is justified for HTVS in this case, or whether one might be able to achieve similar results without performing any DFT simulations at all. We trained a separate random forest classifier using only features obtained from the structure of each catalyst (Table ), which obtained a classification accuracy of 87.0% (±1.8%), corresponding to a 277% increase in the misclassified sites (13.0% compared to 4.7%) relative to the classifier's performance with electronic features. Note that for the distribution of catalysts in this study, 20% of the candidate sites are active and 90% of the candidate molecules contain at least one active site.