Copolymer Informatics with Multi-Task Deep Neural Networks
This addresses the problem of designing new copolymers for materials science applications, but it is incremental as it builds on existing homopolymer methods.
The paper tackled the property prediction challenge for copolymers by extending polymer informatics beyond homopolymers, achieving accurate models using a dataset of over 18,000 data points for temperatures like glass transition.
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.