Moreover, as Figure shows, the correlation also exhibits heteroscedasticity, where there is better correlation for oligomers with lower λ and worse for compounds with greater reorganization energies. This shows that predicting the λ for oligomers with geometric differences between the neutral and cation species is a complex task. In all likelihood there are many possible geometric changes between neutral and cation geometries, and as such the limited training set makes it challenging for the model to properly account for all reorganization in compounds with large λ. Similar heteroscedastic behavior can be seen in the correlation between the tetramers and hexamers (Figure . In principle, some of the heteroscedasticity in the predictions could be reduced by using more, or even only, hexamers in the training of the model. However, calculating the B3LYP λ for hexam- ers is computationally expensive -which runs counter to the benefit of the ML model as a surrogate for the calculations.