These parameters are then fit to recover quantum mechanical forces and partial charges across a range of configurational space for many different combinations of both singlemolecules and molecular clusters encompassing the atomic environments needed for condensedphase simulations. These geometries are created through quantum-chemical constrainedoptimization and through classical MD using learned IP in an active learning loop. The predicted parameters are output as LAMMPS data files for use in molecular dynamics simulations. Additional information about the training scheme and training data generation can be found in Supporting Information Section S1.