Journal
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
Volume 143, Issue 42, Pages 17677-17689Publisher
AMER CHEMICAL SOC
DOI: 10.1021/jacs.1c08181
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Funding
- Beckman Foundation under the Beckman Young Investigator grant
- National Institute of General Medical Sciences [R35GM142666]
- National Science Foundation [OAC-1818253, CHE-0922858, CHE-1828183]
- NSF [ACI-1053575, CHE-1802789, CHE-2041108, DMR110088]
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Modern polymer science faces challenges of multidimensionality, but a computer-guided materials discovery approach, combining automated flow synthesis and machine learning, successfully synthesized 397 unique copolymer compositions.
Modern polymer science suffers from the curse of multidimensionality. The large chemical space imposed by including combinations of monomers into a statistical copolymer overwhelms polymer synthesis and characterization technology and limits the ability to systematically study structure-property relationships. To tackle this challenge in the context of F-19 magnetic resonance imaging (MRI) agents, we pursued a computer-guided materials discovery approach that combines synergistic innovations in automated flow synthesis and machine learning (ML) method development. A software-controlled, continuous polymer synthesis platform was developed to enable iterative experimental-computational cycles that resulted in the synthesis of 397 unique copolymer compositions within a six-variable compositional space. The nonintuitive design criteria identified by ML, which were accomplished by exploring <0.9% of the overall compositional space, lead to the identification of >10 copolymer compositions that outperformed state-of-the-art materials.
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