“…Besides the use of human intervention to rene the automated continuous-ow platforms, algorithm-driven optimization has gained signicant attention in both academia and industry as a way to explore high-dimensional chemical space and achieve optimal conditions with fewer experiments. 107,[130][131][132][133][134][135][136] There are three main types of algorithms applied for selfoptimization experimentation: local optimization algorithms such as design of experiments (DoE) 137,138 and Nelder-Mead simplex, 139,140 global optimization algorithms like SNOBFIT 141,142 and Bayesian Optimizations, 114,130,[143][144][145] and machine learning algorithms like deep reinforcement learning. 146 Jensen et al are pioneers in this growing eld, having developed various versions of automated continuous-ow platforms, including a fridge-size recongurable platform, 147 a 'plug-and-play' platform, 142 and a robotic platform.…”