Exploration of Self-Propelling Droplets Using a Curiosity Driven Robotic Assistant

TL;DR

This paper presents a curiosity-driven robotic system that autonomously explores complex chemical behaviors, discovering diverse droplet motions and responses, thereby advancing formulation chemistry research.

Contribution

The study introduces a curiosity algorithm-based robotic assistant capable of open-ended exploration, leading to the discovery of new droplet behaviors without predefined targets.

Findings

Identified six modes of droplet motion.

Discovered droplets' responses to temperature changes.

Achieved an order of magnitude more behavioral diversity than random search.

Abstract

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.