Polymers are diverse and versatile materials that have met a wide range of material application demands. They come in several flavors and architectures, e.g., homopolymers, copolymers, polymer blends, and polymers with additives. Searching this enormous space for suitable materials with a specific set of property/performance targets is thus non-trivial, painstaking, and expensive. Such a search process can be made effective by the creation of rapid and accurate property predictors. In this work, we present a machine-learning framework to predict the thermal properties of homopolymers, copolymers, and polymer blends. A universal fingerprinting scheme capable of handling this entire polymer chemical class has been developed and a multi-task deep learning algorithm is trained simultaneously on a large dataset of glass transition, melting, and degradation temperatures. The developed models are accurate, fast, flexible, and scalable to other properties when suitable data become available.
@article{arxiv.2303.12938,
title = {Polymer Informatics Beyond Homopolymers},
author = {Shivank S. Shukla and Christopher Kuenneth and Rampi Ramprasad},
journal= {arXiv preprint arXiv:2303.12938},
year = {2024}
}