polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics
This addresses the problem of rapidly identifying suitable polymer candidates for applications, which is incremental as it builds on existing fingerprint schemes with a novel machine learning approach.
The authors tackled the challenge of searching the vast chemical space of polymers for application-specific candidates by developing polyBERT, a complete end-to-end machine-driven polymer informatics pipeline that treats polymer chemical structures as a chemical language. The approach achieves two orders of magnitude faster speed than existing methods while preserving accuracy in property prediction.
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.