In this work, we build on this understanding to choose a set of structural and electronic features which we hypothesize could predict oxygen binding and ORR activity in nitrogen-doped carbon materials. Interpretable molecular features can be used not only to guide the manual search for new catalyst structures by rational design, but also to train machine learning (ML) models that predict activity with very low computational cost. If sufficiently accurate and generalizable, these ML models can be applied to perform high-throughput virtual screening (HTVS) of larger libraries of existing and generated structures than DFT could screen alone, 27 facilitating the discovery of more chemically diverse catalysts with higher activity.