Fig. 1.

Summary of the workflow presented herein. During robotic exploration, oil droplets are placed into a surfactant-containing aqueous phase by a robotic assistant. The droplet behavior is recorded, analyzed by computer vision, and fed back to generate the next experiments via a genetic algorithm, in a closed-loop system. The recipes and data generated from this process are then used for physicochemical analysis, where traditional chemical analysis, machine learning, and archetypal droplet experiments are used to study the behavioral mechanisms and to predict droplet behaviors.