  Modern machine learning force fields (ML-FF) are able to yield energy and
force predictions at the accuracy of high-level $ab~initio$ methods, but at a
much lower computational cost. On the other hand, classical molecular mechanics
force fields (MM-FF) employ fixed functional forms and tend to be less
accurate, but considerably faster and transferable between molecules of the
same class. In this work, we investigate how both approaches can complement
each other. We contrast the ability of ML-FF for reconstructing dynamic and
thermodynamic observables to MM-FFs in order to gain a qualitative
understanding of the differences between the two approaches. This analysis
enables us to modify the generalized AMBER force field (GAFF) by
reparametrizing short-range and bonded interactions with more expressive terms
to make them more accurate, without sacrificing the key properties that make
MM-FFs so successful.
