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polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics

Author

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  • Christopher Kuenneth

    (Georgia Institute of Technology
    University of Bayreuth)

  • Rampi Ramprasad

    (Georgia Institute of Technology)

Abstract

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.

Suggested Citation

  • Christopher Kuenneth & Rampi Ramprasad, 2023. "polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39868-6
    DOI: 10.1038/s41467-023-39868-6
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    Cited by:

    1. Rishi Gurnani & Stuti Shukla & Deepak Kamal & Chao Wu & Jing Hao & Christopher Kuenneth & Pritish Aklujkar & Ashish Khomane & Robert Daniels & Ajinkya A. Deshmukh & Yang Cao & Gregory Sotzing & Rampi , 2024. "AI-assisted discovery of high-temperature dielectrics for energy storage," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    2. Tianyu Wu & Min Zhou & Jingcheng Zou & Qi Chen & Feng Qian & Jürgen Kurths & Runhui Liu & Yang Tang, 2024. "AI-guided few-shot inverse design of HDP-mimicking polymers against drug-resistant bacteria," Nature Communications, Nature, vol. 15(1), pages 1-22, December.
    3. Jinho Chang & Jong Chul Ye, 2024. "Bidirectional generation of structure and properties through a single molecular foundation model," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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