DNA makes an excellent test system for building a prototypical machine learning model for a biological macromolecule. Compared to a protein, for example, the DNA helix has a more regular, predictable structure with considerably less conformational variation due to strict base pairing rules , making DNA structural information easier to sample. Furthermore, DNA naturally breaks down into component parts that make for suitable training structures. And DNA has a small number of these component parts, consisting of only the four bases (A, C, T, and G), sugar rings, and phosphate groups. With these considerations, we successfully constructed a machine learning model that computes ab initio electron densities for DNA structures of arbitrary size, tested for systems with up to 20,000 atoms . However, the model only accounted for DNA by itself (gas-phase DNA) without the inclusion of solvent.