  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.
