As previously mentioned, the electrostatic potential is a useful property that can be derived from the electron density. The electrostatic potential is a key component for studying binding and solvent interactions and is a particularly important property of DNA due to its negatively charged phosphate groups. The electrostatic potential is strongly linked to the DNA base sequence and has been used to explain the site-specific binding of proteins and counterions that are essential for maintaining DNA structure. However, electrostatic potentials are often not calculated from first principles due to system size limitations and may therefore neglect important physics, such as screening and polarization effects. In this article, we present what is to our knowledge the first machine learning model that produces accurate electron densities for large scale DNA structures without having to run costly-or in many cases, unfeasiblequantum simulations. A key factor to proving the utility of the model is developing an effective training protocol and carefully quantifying and contextualizing its error. Finally, with the groundwork of the model established, we present an application to calculate quantum-accurate electrostatic potentials from the machine-learned densities, which can be useful for characterizing DNA-protein binding and solvent interactions.