Autonomous chemistry system identifies high-performing and tunable catalysts

Researchers at North Carolina State University and the University of North Carolina, supported by Eastman Chemical, developed a closed-loop autonomous catalysis platform that discovers and optimizes catalysts. It combines parallel miniaturized pressurized gas–liquid batch reactors with a hierarchical, plate-constrained Bayesian optimization approach that simultaneously tunes discrete variables (like ligand identity) and continuous process conditions. The system identifies high-performing catalyst formulations and reveals how reaction conditions control selectivity, enabling tunable catalysis.

What types of reactions or industries (pharma, fine chemicals, bulk commodities) would benefit most from this mixed-variable, closed-loop optimization, and what constraints or priors would you include to guide it?