Having a tool that can obtain accurate electron densities for DNA in all its forms opens the door for applications that were previously restricted by computational scaling. In principle, any application that requires an electron density should be able to make use of the model. The model has a direct application for calculating quantum-accurate electrostatic potentials, which have been used to study DNA binding interactions with proteins and counterions. When comparing modelderived electrostatic potentials to classical force fields, the machine learning potentials do not show deficiencies at short range. Lastly, this machine learning electron density model has exciting future uses in molecular dynamics. The model could be used to develop more accurate force fields for classical molecular dynamics, or applied directly to calculate Hellmann-Feynman forces for ab initio molecular dynamics.