Polybot: Accelerating Electronic Polymer Discovery through AI/ML and Automation

The increasing demand for flexible, wearable, and smart electronics has propelled the exploration of electronic polymers. To unleash their full potential, understanding the complex relationships between processing, structure, and properties is crucial, along with the development of scalable methods for producing high-quality thin films and devices. This task is particularly challenging due to the intricate interactions between processing parameters, including solution formulation, rheological behavior, and post-treatment procedures. In this presentation, we introduce Polybot, an advanced Autonomous Materials Acceleration Platform (MAP) that seamlessly integrates AI/ML, robotics, and automated characterization techniques for expediting the discovery and optimization of electronic polymers. Polybot exemplifies its prowess through closed-loop studies, demonstrating simultaneous enhancements in polymer processability and performance within a high-dimensional experimental parameter space. Furthermore, we delve into the intriguing realm of small data, addressing the integration of literature-derived insights and physics-based simulations within MAPs. Our discussion will illuminate both the challenges and the promising prospects associated with this integration, showcasing how it enriches the efficacy and robustness of the materials discovery process.