Autonomous Synthesis and Inverse Design of Electrochromic Polymers with High Efficiency and Accuracy

The design and synthesis of functional polymers, aimed
at targeted
properties through specific structures, have long been challenged
by their complex and often nonlinear structure–property relationships.
Key processes, including knowledge accumulation for predictive design
and experimental refinement and validation, are traditionally labor-insensitive
and time-consuming, making it difficult to balance accuracy and efficiency.
Here, we introduce an accelerated, autonomous system for the on-demand
synthesis of electronic polymers that achieves the desired electrochromic
functionality with high accuracy and efficiency. Our approach leverages
large language model-assisted data mining, a physics-informed copolymer
machine learning model, and an AI-driven autonomous robotic workflow
in the Polybot lab. Within 72 h, Polybot autonomously synthesized
electrochromic polymers (ECPs) with targeted, previously-unreported
color values, including green polymers with specific absorption profiles,
precisely fine-tuning copolymer structures with a 5% step size in
comonomer composition within a three-monomer system. A publicly accessible
ECP informatics database has also been created to foster knowledge
exchange.