. While the models are not constrained to predict the number of electrons exactly, they still predict these values with high accuracy, giving errors of much less than one electron out of around 580 total in the test set structures. From the data in Table , the model trained on the entire base pair has a low signed relative error of 0.0036%, suggesting that the distribution of the errors is centered around the 0, with positive and negative errors cancelling out. On the other hand, the signed mean error for the fragment-trained model is more negative with a greater magnitude (-0.020%), suggesting that the fragment-trained model systematically underestimates the number of electrons for a given test structure, although this underestimation is still very small. The underestimation makes sense considering that capping the training fragments with H will contribute fewer electrons compared to the full DNA structures. The underestimation of electrons extends to the tests on longer DNA structures (S9 Table ). Note that in this case, both models underestimate the number of electrons. However, the underestimation in the fragment-trained model is noticeably larger due to the more aggressive fragmentation. Thus, for the fragment-trained model in particular, it may be possible to improve model accuracy by employing a charge equilibration scheme to constrain the model to predict the correct number of electrons.