Data-Driven Multiparameter Optimization for Efficient Screening of Reaction Conditions in Flow and Electrochemical Synthesis

Optimization of reaction conditions is indispensable yet time-consuming in developing new reactions. Advanced modalities such as flow and electrochemical reactions often require substantially more experiments to identify optimal conditions compared to conventional batch processes. While machine learning approaches can efficiently predict suitable conditions, they typically rely on specialized informatics expertise and large, carefully curated datasets. In this review, we highlight the application of data-driven multiparameter screening with small datasets-more accessible to experimental chemists-to the exploration of conditions in flow and electrochemical reactions. Yield mapping with Gaussian process regression enabled the visualization of predicted yields across the design space for an organocatalytic flow domino reaction, allowing the identification of high-yielding conditions with only ten experiments. Similarly, Bayesian optimization (BO)-based multiparameter screening of electrooxidation conditions successfully identified optimal high-yielding conditions within twelve experiments. Categorical BO incorporating one-hot encoding identified six key parameters-including mixer selection-for an organocatalytic flow biaryl synthesis, achieving substantial yield improvement in fifteen experiments. In addition, constrained BO applied to the electrochemical flow synthesis of amino acids accomplished two simultaneous objectives-high yield and reduced energy consumption-with only nine experiments.