  Polymer informatics tools have been recently gaining ground to efficiently
and effectively develop, design, and discover new polymers that meet specific
application needs. So far, however, these data-driven efforts have largely
focused on homopolymers. Here, we address the property prediction challenge for
copolymers, extending the polymer informatics framework beyond homopolymers.
Advanced polymer fingerprinting and deep-learning schemes that incorporate
multi-task learning and meta-learning are proposed. A large data set containing
over 18,000 data points of glass transition, melting, and degradation
temperature of homopolymers and copolymers of up to two monomers is used to
demonstrate the copolymer prediction efficacy. The developed models are
accurate, fast, flexible, and scalable to more copolymer properties when
suitable data become available.
