Two likely sources are responsible for the loss of accuracy when training on smaller fragments. The first is that smaller training structures inherently contain less information about long-range density correlations. Note that for any machine learning model that makes predictions on large-scale biological macromolecules such as DNA, the training structures will necessarily be smaller than the full system structures and thus naturally impose a cut-off in the long-range correlations that can be sampled by the training set. That is to say, there is an intrinsic trade-off between model accuracy and training fragment size, which affects the cost of training the model. For the current study, the fragment structures are two to four times smaller than the entire base-pair step structures, giving a model error increase from ϵ ρ M L = 0.33% up to ϵ ρ M L = 0.53%. Because larger basis sets are required to calculate accurate forces from training structures, the cost savings in using smaller training fragments may be worth the small increase in model error.