More broadly, we envisage QM-to-MM mapping potentials, such as these, providing synergy with traditional force fields. For example, our conclusions concerning force field design protocols can be fed into large-scale fitting efforts, such as the Open Force Field Initiative, to focus efforts in areas where accuracy improvements are expected. This will be particularly important as the community moves towards more advanced functional forms, for which the number of parameters to be fit will only increase. With the advent of machine learning models for parameterising force fields, the force fields developed here could be used to provide regularisation of the parameter fits, to avoid unphysical predictions by the models. Or conversely, machine learning models could be trained to reproduce the outputs of our QM-to-MM mapping procedures to substantially reduce the computational cost of the parameterisation stage. All software required to derive the designed force fields is freely available at .