In addition to parameterizing classical force fields, it is also possible to directly calculate forces from the electron density itself using the Hellmann-Feynman theorem. An advantage for using machine learning models to do this is that long-range forces calculated from densitybased machine learning models converge much quicker with respect to training cluster size compared to forcebased models. While the aug-cc-pvdz basis set used to train the model in this work is not large enough to fulfill the requirements for accurate Hellmann-Feynman forces, the accuracy of the machine learning electron densities suggests that with more complete basis sets the model could be directly applied for ab initio molecular dynamics.