Autonomous Small-Angle Scattering for Accelerated Soft Material Formulation Optimization

Abstract

The pace of soft material formulation (re)development and design is rapidly increasing as both consumers and new legislation demand products that do less harm to the environment while maintaining high standards of performance. To meet this need, we have developed the Autonomous Formulation Lab (AFL), a platform that can automatically prepare and measure the microstructure of liquid formulations using small-angle neutron and X-ray scattering and, soon, a variety of other techniques. Here, we describe the design, philosophy, tuning, and validation of our active learning agent that guides the course of AFL experiments. We show how our extensive in silico tuning results in an efficient agent that is robust to both the number of measurements and signal to noise variation. Finally, we experimentally validate our virtually tuned agent by addressing a model formulation problem: replacing a petroleum-derived component with a natural analog. We show that the agent efficiently maps both formulations and how post hoc analysis of the measured data reveals the opportunity for further specialization of the agent. With the tuned and proven active learning agent, our autonomously guided AFL platform will accelerate the pace of discovery of liquid formulations and help speed us towards a greener future.