Journal
NATURE REVIEWS MATERIALS
Volume 6, Issue 8, Pages 642-644Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41578-021-00282-3
Keywords
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Funding
- NSF CBET award [2009942]
- NIH NIGMS award [1R35GM138296-01]
- Princeton University
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [2009942] Funding Source: National Science Foundation
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The design of new functional polymers relies on exploring their structure-function landscapes. Advances in combinatorial polymer chemistry and machine learning offer exciting opportunities for engineering purpose-fit polymeric materials.
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. 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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