Exploration of Self-Propelling Droplets Using a Curiosity Driven Robotic Assistant
This work addresses the challenge of open-ended exploration in formulation chemistry, offering a method to accelerate unpredictable discoveries, though it is incremental as it builds on existing curiosity algorithms applied to a new domain.
The researchers tackled the problem of exploring complex chemical systems by developing a curiosity-driven robotic assistant that autonomously discovers diverse behaviors in self-propelling oil-in-water droplets, resulting in an order of magnitude more variety than random search and identifying six motion modes through a time-temperature phase diagram.
We describe a chemical robotic assistant equipped with a curiosity algorithm (CA) that can efficiently explore the state a complex chemical system can exhibit. The CA-robot is designed to explore formulations in an open-ended way with no explicit optimization target. By applying the CA-robot to the study of self-propelling multicomponent oil-in-water droplets, we are able to observe an order of magnitude more variety of droplet behaviours than possible with a random parameter search and given the same budget. We demonstrate that the CA-robot enabled the discovery of a sudden and highly specific response of droplets to slight temperature changes. Six modes of self-propelled droplets motion were identified and classified using a time-temperature phase diagram and probed using a variety of techniques including NMR. This work illustrates how target free search can significantly increase the rate of unpredictable observations leading to new discoveries with potential applications in formulation chemistry.