Autonomous reaction Pareto-front mapping with a self-driving catalysis laboratory

Abstract

Ligands play a crucial role in enabling challenging chemical transformations with transition metal-mediated homogeneous catalysts. Despite their undisputed role in homogeneous catalysis, discovery and development of ligands have proven to be a challenging and resource-intensive undertaking. Here, in response, we present a self-driving catalysis laboratory, Fast-Cat, for autonomous and resource-efficient parameter space navigation and Pareto-front mapping of high-temperature, high-pressure, gas–liquid reactions. Fast-Cat enables autonomous ligand benchmarking and multi-objective catalyst performance evaluation with minimal human intervention. Specifically, we utilize Fast-Cat to perform rapid Pareto-front identification of the hydroformylation reaction between syngas (CO and H2) and olefin (1-octene) in the presence of rhodium and various classes of phosphorus-based ligands. By reactor benchmarking, we demonstrate Fast-Cat’s knowledge scalability, essential to fine/specialty chemical industries. We report the details of the modular flow chemistry platform of Fast-Cat and its autonomous experiment-selection strategy for the rapid generation of optimized experimental conditions and in-house data required for supplying machine-learning approaches to reaction and ligand investigations. A self-driving catalysis laboratory, Fast-Cat, is presented for efficient high-throughput screening of high-pressure, high-temperature, gas–liquid reaction conditions using rhodium-catalyzed hydroformylation as a case study. Fast-Cat is used to Pareto map the reaction space and investigate the varying performance of several phosphorus-based hydroformylation ligands.