Another way to contextualize the density prediction error is to compare it to the error from an isolated atom model in which the density is obtained as a superposition of non-interacting, isolated atoms. Representative electron densities produced by the machine learning and isolated atom models (Fig. ) for a randomly selected 4-mer in the test set are shown alongside density differences with the true ab initio densities (Fig. ). The true density prediction error in the machine learning model is ϵ ρtrue = 1.02% compared to ϵ ρtrue = 9.68% in the isolated atom model. An important point about these figures is that even though the machine learning and isolated atom densities appear very similar at a glance, the differences are actually drastic, judging from the disparity of the true errors and the density difference plots. The fact that the machine learning error shows only a slight increase when testing on longer DNA sequences (ϵ ρtrue = 1.00% for a sequence length of 2 vs. ϵ ρtrue = 1.06% for a sequence length of 5) suggests that the cur-rent machine learning model can make accurate predictions on arbitrary DNA structures of any length. In this sense, it is assumed that the base pair step used for training the model encompasses the majority of the interactions-particularly the base pairing and stacking interactions-that affect a DNA structure's electron density. Still, the slight increase in error may be attributable to longer-range interactions that are not sampled in the two base pair training unit. These longer-range interactions could be accounted for by expanding the test set to include 3-mers, 4-mers, and so on. However, note that both the number of base pair combinations and the size of the training unit will increase for larger DNA sequences, which would greatly increase the computational effort for building the training set.