  Synthetic polymers are versatile and widely used materials. Similar to small
organic molecules, a large chemical space of such materials is hypothetically
accessible. Computational property prediction and virtual screening can
accelerate polymer design by prioritizing candidates expected to have favorable
properties. However, in contrast to organic molecules, polymers are often not
well-defined single structures but an ensemble of similar molecules, which
poses unique challenges to traditional chemical representations and machine
learning approaches. Here, we introduce a graph representation of molecular
ensembles and an associated graph neural network architecture that is tailored
to polymer property prediction. We demonstrate that this approach captures
critical features of polymeric materials, like chain architecture, monomer
stoichiometry, and degree of polymerization, and achieves superior accuracy to
off-the-shelf cheminformatics methodologies. While doing so, we built a dataset
of simulated electron affinity and ionization potential values for >40k
polymers with varying monomer composition, stoichiometry, and chain
architecture, which may be used in the development of other tailored machine
learning approaches. The dataset and machine learning models presented in this
work pave the path toward new classes of algorithms for polymer informatics
and, more broadly, introduce a framework for the modeling of molecular
ensembles.
