ChemRxiv Journal
Autonomous process optimization involves the human intervention-free exploration of a range of predefined process parameters in order to improve responses such as reaction yield and product selectivity. Utilizing off-the-shelf components, we developed a closed-loop system capable of carrying out parallel autonomous process optimization experiments in batch with significantly reduced cycle times. Upon implementation of our system in the autonomous optimization of a palladium-catalyzed stereoselective Suzuki-Miyaura coupling, we found that the definition of a set of meaningful, broad, and unbiased process parameters was the most critical aspect of a successful optimization. In addition, we found that categorical parameters such as phosphine ligand were vital to determining the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing an element of bias into the experimental design. In seeking a systematic method for the selection of a diverse set of phosphine ligands fully representative of the chemical space, we developed a strategy that leveraged computed molecular descriptor clustering analysis. This strategy allowed for the successful autonomous optimization of a stereoselective Suzuki-Miyaura coupling between a vinyl sulfonate and an arylboronic acid to selectively generate the E-product isomer in high yield.
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Data-Science driven autonomous process optimization
Melodie Christensen 1,2, Lars P.E. Yunker 1, Folarin Adedeji 2, Florian Häse 3,4,5,7,9, Loïc M. Roch 3,4,5,9, Tobias Gensch 6, Gabriel dos Passos Gomes 4,5,7, Tara Zepel 1, Matthew S. Sigman 6, Alán Aspuru-Guzik 3,4,5,7,8 and Jason E. Hein 1,9
1. Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
2. Department of Process Research and Development, Merck & Co., Inc., Rahway, NJ 07065, United States
3. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, United States
4. Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
5. Department of Computer Science, University of Toronto, Toronto, Ontario M5T 3A1, Canada
6. Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
7. Vector Institute for Artificial Intelligence, Toronto, Ontario M5S 1M1, Canada
8. Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada
9. ChemOS Sàrl, Lausanne, Vaud 1006, Switzerland
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ChemRxiv Journal
https://chemrxiv.org/articles/preprint/Data-science_driven_autonomous_process_optimization/13146404/1
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