(569av) Discrete Homogeneous Catalyst Optimization Using an Autonomous Self-Driving Flow Reactor

Machine learning (ML) and flow chemistry are being leveraged in chemical reaction engineering to optimize complex processes as well as to reduce discovery and development. Recently, there has been a sizable increase in the use of ML algorithms to both optimize and model complex reaction systems. ML algorithms can help to rapidly identify patterns in high-dimensional reaction spaces, predict reaction outcomes, and optimize operating conditions. These data-driven approaches help to effectively leverage large amounts of data generated by self-driving lab approaches.

One of the primary applications of ML in reaction engineering is in the development of predictive response models. These models can be used to form a digital twin of the chemical reaction and predict process outcomes, reducing the number of experiments needed to achieve the desired reaction outcome through optimization, lowering the cost and time associated with catalyst development.

Flow chemistry is another powerful tool that can reduce the time and cost associated with the discovery and development of chemical processes. Automated flow reactors can provide superior control over the reaction conditions, minimize human error, intensify transport rates, and improve process consistency. Furthermore, automation can be coupled to in-line monitoring and characterization of the reaction in real-time, providing feedback to adjust the reaction conditions and optimize the reaction process in a closed-loop format.

In this work, we extend our recent efforts of reaction condition optimization in a self-driving flow reactor to include discrete variables such as ligand structure. Extending the scope of the optimization to include ligand structure allows for the identification of an optimal ligand species given a ligand library and hydroformylation reaction target. The automated reactions in parallel batch and flow are leveraged alongside the ML-based experimental selection to perform autonomous regioselectivity tuning and optimization as well as developing a molecular structure-informed digital twin of the reaction.