The random forest regression model, as implemented in the scikit-learn package, has a feature importance function 32 , which enables exploration of the features in the representation that contribute the most to the model (Figure ). It is clear that the most important feature is the average inter-ring bond length of the neutral oligomer, as calculated using xTB-GFN2. While the bond lengths are expected to change going from the neutral to cation geometries, this is a surprising effect as the correlation between the neutral inter-ring bond length and the B3LYP-calculated λ is weak (R 2 = 0.233, Figure ). Much like the overall reorganization energies, the correlation between GFN2-computed and B3LYP-computed inter-ring bond lengths shows the same heteroscedasticity, which may explain some of the feature importance. The second most important feature is the π-system size descriptor, which agrees with the hypotheses that bigger πconjugated systems promote lower λ. The third most important feature is the ECFP bit number 1019, which indicates the existence of an sp 3 hybridized carbon in the oligomer (Figure ). Two possible explanations exist for this feature -that a sp 3 hybridized carbon breaks conjugation and as discussed below, the CH 2 group may promote a less planar conformation. Interestingly, the monomer numbers, although used as a categorical feature with a seemingly arbitrary assignment meant for naming only, do appear to contribute to the model as the fourth and sixth most important features. The rest of the features are the other geometrical information we encoded into the representation, followed by the rest of the ECFP bits which minimally contribute to the model.