  One of the grand challenges of utilizing machine learning for the discovery
of innovative new polymers lies in the difficulty of accurately representing
the complex structures of polymeric materials. Although a wide array of
hand-designed polymer representations have been explored, there has yet to be
an ideal solution for how to capture the periodicity of polymer structures, and
how to develop polymer descriptors without the need for human feature design.
In this work, we tackle these problems through the development of our periodic
polymer graph representation. Our pipeline for polymer property predictions is
comprised of our polymer graph representation that naturally accounts for the
periodicity of polymers, followed by a message-passing neural network (MPNN)
that leverages the power of graph deep learning to automatically learn
chemically-relevant polymer descriptors. Across a diverse dataset of 10 polymer
properties, we find that this polymer graph representation consistently
outperforms hand-designed representations with a 20% average reduction in
prediction error. Our results illustrate how the incorporation of chemical
intuition through directly encoding periodicity into our polymer graph
representation leads to a considerable improvement in the accuracy and
reliability of polymer property predictions. We also demonstrate how combining
polymer graph representations with message-passing neural network architectures
can automatically extract meaningful polymer features that are consistent with
human intuition, while outperforming human-derived features. This work
highlights the advancement in predictive capability that is possible if using
chemical descriptors that are specifically optimized for capturing the unique
chemical structure of polymers.
