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

About

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.

Christopher Kuenneth, Rampi Ramprasad• 2022

Related benchmarks

TaskDatasetResultRank
Chemical Property PredictionPolymers (5-fold cross-val)
Eea R2 Score0.91
50
Ionization energy (Eib) predictionpolymer electronic property dataset (test)
RMSE (eV)0.443
18
Glass transition temperature (Tg) prediction381-polymer (test)
RMSE (K)25.52
10
Molecular Property Prediction (Eea)Polymer
RMSE0.308
10
Molecular Property Prediction (Ei)Polymer
RMSE0.525
10
Molecular Property Prediction (Xc)Polymer
RMSE17.646
10
Molecular Property Prediction (etac)Polymer
RMSE0.131
10
Polymer property predictionEi Ionization energy S1 (test)
R2 Score (Test)0.794
9
Polymer property predictionEPS Dielectric constant S1 (test)
Test R20.674
9
Static dielectric constant (EPS) predictionpolymer electronic/optical/physical property dataset (test)
RMSE0.626
9
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