posted on 2021-06-29, 16:36authored byChristopher Kuenneth, William Schertzer, Rampi Ramprasad
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 multitask 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.