We used principal component analysis (PCA) to reduce the dimensionality of the COF feature vectors {x 1 , ..., x 609 } from 14 to two, for visualization. Each panel in Fig. shows the first two principal components of COF feature space; each point represents a COF, colored according to its high-fidelity Xe/Kr selectivity. Note, the COFs with the largest high-fidelity Xe/Kr selectivities tend to concentrate in the upper-right region of COF PC space.