Broad chemical transferability in structure-based coarse-graining

TL;DR

This paper introduces a novel structure-based coarse-graining method that achieves chemical transferability across many molecules by combining atomic representations, unsupervised learning, and extensive force-matching over multiple states.

Contribution

It presents a transferable bottom-up coarse-grained modeling approach that maintains structural fidelity while being applicable to a wide range of chemical compounds.

Findings

The CG model outperforms single-state models in structural accuracy.

A set of 19 representative molecules effectively encodes local environments.

Extended-ensemble parametrization yields a more general and smoother force field.

Abstract

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.