For example, intermolecular perturbation theory may be used to separate the full QM interaction energy into physically motivated components, or machine learning based potentials may be trained on QM energies and forces. These methods are attractive because they remove the empiricism of the force field approach, but they are computationally more expensive, and unless they fully incorporate, for example, many body and quantum nuclear effects they are unlikely to reproduce condensed phase observables with sufficient accuracy.