Self-driving Lab (Polybot) for electronic polymer discovery

Autonomous platform is the integration of high-throughput robotic experimentation and characterization with data-driven model development to guide the search for targeted formulation and processing conditions. While autonomous/self-driving laboratories have made great advances in pharmaceuticals, hard materials, and organic small molecules, the development of such systems for polymeric materials is in a relatively nascent stage. This research aims to develop an autonomous discovery platform in Polybot self-driving lab that allows us to rapidly go from a polymer material concept to realized manifestations of final, testable materials targeted at relevant properties. In this talk, I will focus on the autonomous discovery of electrochromic properties of polymers that have full-color tunable optical properties and reversibly modulated optical transmission under applied potentials for energy-saving and color-tunable applications. I will also talk about closed-loop solution processing electronic polymers for desirable functions. Our approach involves automated condition optimization, data mining, and structure-property prediction model building. By integrating of Globus data management system, which can interact with all the required experimental equipment, our predictive ML model can guide the autonomous discovery, facilitating non-human intervention throughout the close-loop exploration process for a new polymer formulation with targeted electronic properties.