Principal component analysis (PCA) is a mathematical tool that can be applied to identify which independent variables contribute most strongly to the variance of a dependent variable, and to elucidate relations between independent variables in how they cause this variance. We applied PCA to quantify which functional group substitution locations most strongly affect the intermediate reaction energies of ORR and to determine similarities between functional groups in how they affect these energies. Section 2 of the SI contains a detailed discussion of the mathematics of how we applied PCA within this context.