Journal
COMMUNICATIONS CHEMISTRY
Volume 4, Issue 1, Pages -Publisher
NATURE RESEARCH
DOI: 10.1038/s42004-021-00550-x
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Funding
- Defense Advanced Research Projects Agency (DARPA) [HR00111920027]
- University of British Columbia
- Canada Foundation for Innovation [CFI-35883]
- NSERC [RGPIN-2016-04613]
- Canada Foundation for Innovation
- Government of Ontario, Ontario Research Fund-Research Excellence
- University of Toronto
- Department of Navy by the Office of Naval Research [N00014-19-1-2134]
- Natural Resources Canada
- Canada 150 Research Chairs program
- NSF under the CCI Center for Computer Assisted Synthesis [CHE-1925607]
- department of Process R&D at Merck & Co., Inc., Kenilworth, NJ, USA
- Tata Sons Limited-Alliance Agreement [A32391]
- Natural Sciences and Engineering Research Council of Canada (NSERC)
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An automated closed-loop system was developed to optimize a stereoselective Suzuki-Miyaura reaction using a machine learning algorithm. The study found that defining a set of meaningful, broad, and unbiased process parameters is crucial for successful optimization, with categorical parameters such as phosphine ligands playing a critical role in determining reaction outcomes.
An automated closed-loop system optimizes a stereoselective Suzuki-Miyaura reaction using a machine learning algorithm that incorporates unbiased and categorical process parameters. Autonomous process optimization involves the human intervention-free exploration of a range process parameters to improve responses such as product yield and selectivity. Utilizing off-the-shelf components, we develop a closed-loop system for carrying out parallel autonomous process optimization experiments in batch. Upon implementation of our system in the optimization of a stereoselective Suzuki-Miyaura coupling, we find that the definition of a set of meaningful, broad, and unbiased process parameters is the most critical aspect of successful optimization. Importantly, we discern that phosphine ligand, a categorical parameter, is vital to determination of the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing bias into the experimental design. In seeking a systematic method for selecting a diverse set of phosphine ligands, we develop a strategy that leverages computed molecular feature clustering. The resulting optimization uncovers conditions to selectively access the desired product isomer in high yield.
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