SDLs and MAPs represent recent advances in the way scientific research is conducted by combining and leveraging the advantages of machine learning, lab automation, and robotics in modular platforms that can help conducting experiments autonomously. Lab automation and robotics can increase the efficiency of discovering and developing new materials through faster experimentation, reduce costs, free resources by allowing researchers to focus on creative tasks rather than repetitive and laborious manual labor tasks, and increase the reliability and reproducibility of experiments, which is of great interest considering the reproducibility crisis in science . Incorporating machine learning and artificial intelligence into the process will considerably speed up data processing and interpretation, e.g., by helping to reduce bottlenecks in evaluating characterization data , and help mapping the vast chemical reaction space more efficiently to arrive at user-defined goals or (pareto) optima faster and more efficiently .