(380e) Optimization of Continuous Organic Synthesis By Integrating Automation, Machine Learning, and Robotics.

Case studies highlight strategies for optimization of continuous organic synthesis (flow chemistry) enabled by integration of computer computer-aided synthesis planning (CASP), automation, robotics, and process analytic tools. Examples include catalytic reactions involving solid substrates, catalysts, and inorganic bases, photochemical transformations, and multistep syntheses. Particular emphasis is given to optimization of a CASP proposed and human-refined multistep synthesis route on a modular, robotic flow synthesis platform with integrated process analytical technologies for data-rich experimentation. Multi-objective Bayesian optimization identifies optimal values for categorical and continuous process variables in the multistep route. The platform’s modularity, robotic reconfigurability, and flexibility for convergent synthesis are shown to be essential for allowing variation of downstream residence time in multistep flow processes and controlling the order of addition to minimize undesired reactivity. Overall, the presentation aims to demonstrate how machine assistance in performing repetitive experimental procedures and data collection enhances focus on domain knowledge, critical interpretation of data, and creative problem-solving.