Along with predicting electrostatic potentials, we suggest additional applications for the machine learning electron density model. One application is to use the model to develop more accurate classical force fields to simulate processes such as DNA self-assembly that cannot be adequately captured by more general DNA force fields. Partial charges in classical force fields are typically parameterized from quantum calculations using techniques such as RESP, but the underlying quantum calculations are often restricted in size and might not best reflect the macromolecule as a whole. The machine learning density model would allow larger reference calculations on a wider variety of relevant conformations to be used for charge fitting, making it possible to fit partial charges much more accurately. In addition, the model could be used for "on-the-fly" charge reassignment simi-lar to QM/MM approaches. This would enable force fields to more accurately model the diversity of conformations that large DNA molecules can sample.