Data-science driven autonomous process optimization

• Published in: Communications chemistry Vol. 4; no. 1; pp. 112 - 12
• Main Authors: 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.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.08.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 639/638/549/933; 639/638/77/885; 639/638/905; Algorithms; Chemical reactions; Chemistry; Chemistry and Materials Science; Chemistry/Food Science; Closed loop systems; Clustering; Coupling (molecular); Data science; Design of experiments; Feedback control; Ligands; Machine learning; Optimization; Phosphines; Process parameters; Selectivity; Stereoselectivity
• ISSN: 2399-3669; 2399-3669
• DOI: 10.1038/s42004-021-00550-x

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. An automated closed-loop system optimizes a stereoselective Suzuki-Miyaura reaction using a machine learning algorithm that incorporates unbiased and categorical process parameters.