  Electronic structure methods offer in principle accurate predictions of
molecular properties, however, their applicability is limited by computational
costs. Empirical methods are cheaper, but come with inherent approximations and
are dependent on the quality and quantity of training data. The rise of machine
learning (ML) force fields (FFs) exacerbates limitations related to training
data even further, especially for condensed-phase systems for which the
generation of large and high-quality training datasets is difficult. Here, we
propose a hybrid ML/classical FF model that is parametrized exclusively on
high-quality ab initio data of dimers and monomers in vacuum but is
transferable to condensed-phase systems. The proposed hybrid model combines our
previous ML-parametrized classical model with ML corrections for situations
where classical approximations break down, thus combining the robustness and
efficiency of classical FFs with the flexibility of ML. Extensive validation on
benchmarking datasets and experimental condensed-phase data, including organic
liquids and small-molecule crystal structures, showcases how the proposed
approach may promote FF development and unlock the full potential of classical
FFs.
