for B-DNA. It is, therefore, not surprising that the model error is slightly higher (ϵ ρtrue = 1.24%) although it remains close to that of the regular B-DNA structure (ϵ ρtrue = 1.15%). For the A-and Z-DNA structures, the error increases to ϵ ρtrue ∼ 1.75%. Considering that Aand Z-DNA have significantly different structural motifs (Z-DNA features a left-handed helix, for instance), the increase in error is expected. Even so, because of the relatively small errors, we conclude that the model trained only on B-DNA structures can reasonably extrapolate to other forms of DNA. The error is comparable to the ϵ ρ M L = 1.5% reported in another extrapolation study, which predicted polypeptide densities from a model trained on a database of biofragments. Of course, the error can be decreased by broadening the training set to include samples from A-and Z-DNA forms. The modest increase in error for A-DNA and Z-DNA suggests that the amount of additional training data necessary to accurately model these structures is likely to be small. Finally, we showcase the capability of our constructed density prediction model and point the way toward future machine learning studies by presenting model-predicted densities for a handful of extremely large DNA structures. These include the Drew-Dickerson dodecamer (Fig. ), a stacked four-way junction (Fig. ), a nucleosome core particle (Fig. ), and an example of a 2D wireframe DNA origami structure (Fig. ). The largest of these structures contains 108,654 electrons, placing it far beyond the scope of traditional quantum methods. The times to compute a density for these structures with the model are presented in Fig. . Plotted on a log-log scale, the slope of the trendline should correspond to the order of scaling O(N ). Based on a slope of N = 1.17, the scaling for the machine learning model is essentially linear.