While KDE plots in Figure -C quantitatively show the influence of composition on structure, 2D PCA in Figure -F seems to reflect that there are three distinct groups within the dataset (Figure ) when we featurize the data using all known values (Rg, Rh, Rg/Rh, molecular weight, total charge, total positive charge, total negative charge, log P, and HLB) (representation of explained variance is provided in Figure ). We also observe that when the data is featurized by only polymer design features (DP, mol% DMA, mol% TMAEMA, mol% MMA, mol% BMA, mol% NHS methacrylic acid, mol% DBCO, mol% PEG, mol% EPO, mol% WDCL, and molecular weight) and colored by Porod exponent and Rg (Figure ), there does not seem to be a clear gradient or dependency. These illustrations reveal that SCNP structural design contains non-linear complexities that cannot be explained by simple feature correlations alone. Additional twodimensional KDE plots are provided for only chemical descriptors and experimentally derived features (Figures and). with Rg/Rh of PDB proteins, copolymer backbones, and EPO-SCNPs along with PDB proteins, PEG-SCNPs, and WDCL-SCNPs are provided. (C) PCA was completed for all groups using all features (Rg, Rh, Porod exponent, Porod volume, Rg/Rh, molecular weight, total charge, total positive charge, total negative charge, log P, and HLB) with explained variance of 0.608. PCA was also done using only polymer design features (DP, % DMA, % TMAEMA, % MMA, % BMA, % NHS methacrylic acid, % DBCO, % PEG, % EPO, % WDCL, and molecular weight), colored by (D) Porod exponent and (E) Rg.