In order for a machine learning model to make predictions with any accuracy, it must be properly trained. Practically speaking, to make predictions for a large biomolecular structure such as DNA, the training data must be small enough to calculate quickly yet complex enough to capture the interesting physics that undergird the larger structure. In principle, the machine learning model learns generalizable features about atomic environments, such as bonding interactions, that can be thoroughly sampled by the training set. With these considerations, we decided on the base pair step of B-DNA as the fundamental training unit. The base pair step is wellsuited for training DNA because it is the smallest unit that captures three key physical interactions: the hydrogen bonds between complementary base pairs (A/T and G/C) that hybridize the two strands, the base stacking interactions between adjacent bases along a strand that stabilize the DNA double helical structure, and the covalent linkages between nucleotide components that form the DNA backbone.