Copolymer Informatics with Multi-Task Deep Neural Networks

TL;DR

This paper develops advanced deep learning models for copolymer property prediction, extending polymer informatics beyond homopolymers with high accuracy and scalability using multi-task and meta-learning techniques.

Contribution

It introduces a novel framework combining polymer fingerprinting with multi-task and meta-learning for copolymer property prediction, expanding the scope of polymer informatics.

Findings

Models accurately predict copolymer properties.

Framework is fast, flexible, and scalable.

Demonstrated on over 18,000 data points.

Abstract

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.