Appendix). Across a holdout test set that includes all possible combinations of base-pair step structures (for details on the test set structures, see S5 plot learning curves based on the number of heavy (non-H) atoms in the training data (Fig. ). For both models, the learning curves are essentially linear on a log-log scale, which show that the models meaningfully learn from their training data . As expected, compared to the previous model trained on the entire base-pair step, the fragment-trained model has larger density prediction errors (ϵ ρ M L (%)) and also learns less per heavy atom in the training set, judging from its shallower slope of its learning curve. This is not surprising because the fragmented training structures are represented in the network as smaller graphs with fewer connections and thus contain inherently less information for the model to learn. Despite the loss of accuracy, the fragment-trained model still produces highly accurate densities that are close to the state-of-the-art, with an error as low as ϵ ρ M L = 0.53% for the model trained with 300,000 heavy atoms. Some of the current most accurate machine learning density models report errors as low as 0.3% for small molecules (less than 25 atoms) ranging up to 2.5% for more complex molecules and small proteins . We note that the DNA structures in our test set contain around 125 atoms and are considerably more complex than the small molecules tested in the above studies, yet the error of ϵ ρ M L = 0.33% for the model trained on the entire base-pair step compares well with these studies. Furthermore, when testing on larger DNA structures, the fragment-trained model shows the same behavior as the previous model in that the error flattens out with progressively longer base sequences, approaching a value of around ϵ ρtrue = 1.08% (Fig. ). This suggests that both the fragmented and entire base-pair step models are suitable for making predictions on larger DNA sequences with a negligible loss of accuracy.