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Data-science driven autonomous process optimization

revised on 02.11.2020, 16:51 and posted on 03.11.2020, 09:53 by Melodie Christensen, Lars Yunker, Folarin Adedeji, Florian Häse, Loic Roch, Tobias Gensch, Gabriel dos Passos Gomes, Tara Zepel, Matthew Sigman, Alan Aspuru-Guzik, Jason Hein

Autonomous process optimization involves the human intervention-free exploration of a range of pre-defined 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.


Email Address of Submitting Author


The University of British Columbia and Merck & Co. Inc.



ORCID For Submitting Author


Declaration of Conflict of Interest

There are no conflicts of interest to declare.