Co-crystals are a
highly interesting material class as varying
their components and stoichiometry in principle allows tuning supramolecular
assemblies toward desired physical properties. The
in silico
prediction of co-crystal structures represents a daunting task,
however, as they span a vast search space and usually feature large
unit cells. This requires theoretical models that are accurate and
fast to evaluate, a combination that can in principle be accomplished
by modern machine-learned (ML) potentials trained on first-principles
data. Crucially, these ML potentials need to account for the description
of long-range interactions, which are essential for the stability
and structure of molecular crystals. In this contribution, we present
a strategy for developing Δ-ML potentials for co-crystals, which
use a physical baseline model to describe long-range interactions.
The applicability of this approach is demonstrated for co-crystals
of variable composition consisting of an active pharmaceutical ingredient
and various co-formers. We find that the Δ-ML approach offers
a strong and consistent improvement over the density functional tight
binding baseline. Importantly, this even holds true when extrapolating
beyond the scope of the training set, for instance in molecular dynamics
simulations under ambient conditions.