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Machine learning in combinatorial polymer chemistry

Nature Reviews Materials volume 6, pages 642–644 (2021)Cite this article

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The design of new functional polymers depends on the successful navigation of their structure-function landscapes. Advances in combinatorial polymer chemistry and machine learning provide exciting opportunities for the engineering of fit-for-purpose polymeric materials.

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Fig. 1: Robotic systems for autonomous structure-function testing in combinatorial polymer libraries.

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Acknowledgements

A.J.G. was supported by NSF CBET award number 2009942 and NIH NIGMS award number 1R35GM138296-01. M.A.W. acknowledges support from Princeton University.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA

    Adam J. Gormley

  2. Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, USA

    Michael A. Webb

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  1. Adam J. Gormley
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  2. Michael A. Webb
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Both authors contributed equally to the preparation of the manuscript.

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Correspondence to Adam J. Gormley or Michael A. Webb.

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The authors declare no competing interests.

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Related links

CHEMnetBASE: http://poly.chemnetbase.com/

Critical Assessment of Protein Structure Prediction Competition: https://predictioncenter.org

Materials data facility: https://materialsdatafacility.org/

Materials Genome Initiative: https://www.mgi.gov/

PoLyInfo: https://polymer.nims.go.jp/en/

Polymer Genome: https://www.polymergenome.org/

Polymer Property Predictor and Database: https://pppdb.uchicago.edu/

Protein Data Bank: http://www.wwpdb.org/

QM9: http://quantum-machine.org/datasets/

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Gormley, A.J., Webb, M.A. Machine learning in combinatorial polymer chemistry. Nat Rev Mater 6, 642–644 (2021). https://doi.org/10.1038/s41578-021-00282-3

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