Abstract
Machine-learning assisted simultaneous multi-parameter screening is described. After brief experimental screening, Gaussian process regression could rapidly predict the optimal conditions for a sequential organocatalyzed Rauhut-Currier and ［3+2］ annulation sequence in a flow reaction system. In addition, Bayesian optimization was successfully applied to an electrochemical oxidation of amines to imines. The concentration of the substrate and electrolite, current, reaction time, and reaction temperature were optimized using six experimental data.
