  Simulations with an explicit description of intermolecular forces using
electronic structure methods are still not feasible for many systems of
interest. As a result, empirical methods such as force fields (FF) have become
an established tool for the simulation of large and complex molecular systems.
However, the parametrization of FF is time consuming and has traditionally been
based largely on experimental data, which is scarce for many functional groups.
Recent years have therefore seen increasing efforts to automatize FF
parametrization and a move towards FF fitted against quantum-mechanical
reference data. Here, we propose an alternative strategy to parametrize
intermolecular interactions, which makes use of machine learning and
gradient-descent based optimization while retaining a functional form founded
in physics. This strategy can be viewed as generalization of existing FF
parametrization methods. In the proposed approach, graph neural networks are
used in conjunction with automatic differentiation to parametrize physically
motivated models to potential-energy surfaces, enabling full automatization and
broad applicability in chemical space. As a result, highly accurate FF models
are obtained which retain the computational efficiency, interpretability and
robustness of classical FF. To showcase the potential of the proposed method,
both a fixed-charge model and a polarizable model are parametrized for
intermolecular interactions and applied to a wide range of systems including
dimer dissociation curves and condensed-phase systems.
