Data-science driven autonomous process optimization
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 02, 2021
- Source ID
- 10.1038/s42004-021-00550-x
Entities
People
- Alán Aspuru-Guzik
- Florian Häse
- Folarin Adedeji
- Gabriel Dos Passos Gomes
- Jason E Hein
- Lars P E Yunker
- Loïc M Roch
- Matthew Sigman
- Melodie Christensen
- Tara Zepel
- Tobias Gensch
Organizations
- Canada Foundation for Innovation
- Natural Resources Canada
- Natural Sciences and Engineering Research Council
- Office of Naval Research
- Tata Sons
- United States Department of Defense