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Machine learning in combinatorial polymer chemistry

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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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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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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Critical Assessment of Protein Structure Prediction Competition:

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Gormley, A.J., Webb, M.A. Machine learning in combinatorial polymer chemistry. Nat Rev Mater 6, 642–644 (2021).

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