These range from the choice of functional form to use for the final force field, to the choice of underlying QM method to compute the electron density, how to partition it between atoms, and how to map the partitioned density to force field parameters. These choices may be regarded as hyperparameters of the model. Drawing on lessons from the machine learning field, separate models for each hyperparameter should be systematically trained and tested.