Advancing Polymer Innovation with Polybot: An AI-Guided Robotic Laboratory

Autonomous laboratories that integrate high-throughput synthesis, processing, and characterization with data-driven modeling offer a powerful pathway to accelerate materials discovery and reveal structure–property relationships. We are developing Polybot, an AI-guided autonomous laboratory with particular strength in addressing the complexity of polymer systems, where subtle changes in formulation or processing can yield large variations in performance, and where synthesis, processing history, and multiscale morphology are deeply intertwined.

This talk will highlight Polybot-enabled advances in the autonomous discovery of electronic polymers, including the inverse design of optical properties in copolymer structures, automated solution processing to modulate solid-state morphology and resulting electronic properties, and data-driven identification of morphological design principles that enable optimized mixed ionic–electronic conductivity in polymers.I will also discuss ongoing efforts to evolve Polybot into a more adaptive, community-facing platform through enhanced human-machine interfaces and the development of curated polymer datasets.