ma1c00728_si_001.pdf (82.18 kB)
Download fileCopolymer Informatics with Multitask Deep Neural Networks
journal contribution
posted on 2021-06-29, 16:36 authored by Christopher Kuenneth, William Schertzer, Rampi RamprasadPolymer
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.
History
Usage metrics
Categories
Keywords
copolymer propertiesCopolymer Informaticsproperty prediction challengecopolymer prediction efficacypolymer informatics frameworkcopolymersdeep-learning schemesdata-driven effortshomopolymersglass transitiondegradation temperatureAdvanced polymer fingerprinting18 00018 000 data pointsMultitask Deep Neural Networks Poly...