1. Data-science driven autonomous process optimization
- Author
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Alán Aspuru-Guzik, Tobias Gensch, Florian Häse, Gabriel dos Passos Gomes, Tara Zepel, Lars P. E. Yunker, Folarin Adedeji, Jason E. Hein, Loïc M. Roch, Matthew S. Sigman, and Melodie Christensen
- Subjects
Mathematical optimization ,010405 organic chemistry ,Process (engineering) ,Computer science ,General Chemistry ,010402 general chemistry ,01 natural sciences ,Biochemistry ,0104 chemical sciences ,Set (abstract data type) ,Range (mathematics) ,Chemistry ,Product (mathematics) ,Materials Chemistry ,Feature (machine learning) ,Environmental Chemistry ,Process optimization ,Cluster analysis ,Categorical variable ,QD1-999 - Abstract
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.
- Published
- 2021