Autonomous Experimentation

Soft materials often confront us with high-dimensional compositional and processing parameter landscapes that become intractable for traditional experimental workflows. To help accelerate the mapping and utilization of structure-property-function relationships in soft materials, we are developing SoftAE, a high-throughput, multimodal autonomous experimentation platform.

In addition to the physical platform itself, we intend to contribute a model framework for enabling impactful AE-driven research in a broad spectrum of research institutions. Assembled with a combination of modular, open-source, and custom-prototyped hardware and driven by a central Python interface, the system integrates formulation, optical microscopy, an environmental chamber, patterned optical fields, and electrochemical measurements. Coupling this to tailorable implementation of machine learning algorithms and agentic workflows allows autonomy in experimental decision-making.

In tandem with Membrane Separations, we are also interested in applying high-throughput and autonomous experimentation to explore polymer and composite formulations for efficient charge transport.