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Data-science driven autonomous process optimization
Communications Chemistry  (IF6.581),  Pub Date : 2021-08-02, DOI: 10.1038/s42004-021-00550-x
Christensen, Melodie, Yunker, Lars P. E., Adedeji, Folarin, Häse, Florian, Roch, Loïc M., Gensch, Tobias, dos Passos Gomes, Gabriel, Zepel, Tara, Sigman, Matthew S., Aspuru-Guzik, Alán, Hein, Jason E.

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.