Designing Coarse-Grained Representations for Soft Materials using Attentive Message-Passing

Bottom-up methods for coarse-grained (CG) molecular modeling are needed to establish rigorous links between atomistic reference data and reduced molecular representations. For a given molecule, the ideal CG representation is a function of both the conformational ensemble of the system and the target physical observable(s) to be reproduced at the CG resolution, and serves as the information bottleneck limiting the precision of determining structure-function relationships. However, there is an absence of algorithms for selecting CG representations of molecules from which complex properties, including molecular electronic structure, can be accurately modeled. We describe how Graph Neural Network (GNN) architectures, which are already commonly used for molecular property prediction, can be modified using attention functions coupled to the loss of information associated with the coarsening of a representation. These methods can be used to perform arbitrary property predictions while regressing on automatically-identified CG representations or inferring representations over diverse chemistries. Sampled attention embeddings provide an interpretable framework for quantifying the space of effective representations and the collective variables influencing molecular properties.