  Compared to top-down coarse-grained (CG) models, bottom-up approaches are
capable of offering higher structural fidelity. This fidelity results from the
tight link to a higher-resolution reference, making the CG model chemically
specific. Unfortunately, chemical specificity can be at odds with
compound-screening strategies, which call for transferable parametrizations.
Here we present an approach to reconcile bottom-up, structure-preserving CG
models with chemical transferability. We consider the bottom-up CG
parametrization of 3,441 C$_7$O$_2$ small-molecule isomers. Our approach
combines atomic representations, unsupervised learning, and a large-scale
extended-ensemble force-matching parametrization. We first identify a subset of
19 representative molecules, which maximally encode the local environment of
all gas-phase conformers. Reference interactions between the 19 representative
molecules were obtained from both homogeneous bulk liquids and various binary
mixtures. An extended-ensemble parametrization over all 703 state points leads
to a CG model that is both structure-based and chemically transferable.
Remarkably, the resulting force field is on average more structurally accurate
than single-state-point equivalents. Averaging over the extended ensemble acts
as a mean-force regularizer, smoothing out both force and structural
correlations that are overly specific to a single state point. Our approach
aims at transferability through a set of CG bead types that can be used to
easily construct new molecules, while retaining the benefits of a
structure-based parametrization.
