  Polymers are a vital part of everyday life. Their chemical universe is so
large that it presents unprecedented opportunities as well as significant
challenges to identify suitable application-specific candidates. We present a
complete end-to-end machine-driven polymer informatics pipeline that can search
this space for suitable candidates at unprecedented speed and accuracy. This
pipeline includes a polymer chemical fingerprinting capability called polyBERT
(inspired by Natural Language Processing concepts), and a multitask learning
approach that maps the polyBERT fingerprints to a host of properties. polyBERT
is a chemical linguist that treats the chemical structure of polymers as a
chemical language. The present approach outstrips the best presently available
concepts for polymer property prediction based on handcrafted fingerprint
schemes in speed by two orders of magnitude while preserving accuracy, thus
making it a strong candidate for deployment in scalable architectures including
cloud infrastructures.
