Data-science driven autonomous process optimization

• DOI: 10.1038/s42004-021-00550-x
• Publication Date: 2021-08-02T10:03:01Z
• Authors: Melodie Christensen; Lars P. E. Yunker; Folarin Adedeji; Florian Häse; Loïc M. Roch; Tobias Gensch; Gabriel dos Passo...; Tara Zepel; Matthew S. Sigman; Alán Aspuru-Guzik; Jason E. Hein
• Keywords: Categorical variable; Intuition

Autonomous process optimization involves the human intervention-free exploration of a range parameters to improve responses such as product yield and selectivity. Utilizing off-the-shelf components, we develop closed-loop system for carrying out parallel autonomous experiments in batch. Upon implementation our stereoselective Suzuki-Miyaura coupling, find that definition set meaningful, broad, unbiased is most critical aspect successful optimization. Importantly, discern phosphine ligand, categorical parameter, vital determination reaction outcome. To date, parameter selection has relied on chemical intuition, potentially introducing bias into experimental design. In seeking systematic method selecting diverse ligands, strategy leverages computed molecular feature clustering. The resulting uncovers conditions selectively access desired isomer high yield.