The Alchemist, the Scientist, and the Robot: Exploring the Potential of Human‐AI Symbiosis in Self‐Driving Polymer Laboratories

Polymer chemistry research has progressed through three methodological eras: the alchemist's intuitive trial-and-error, the scientist's rule-based design, and the robot's algorithm-guided automation. While approaches combining combinatorial chemistry with statistical design of experiments offer a systematic approach to polymer discovery, they struggle with complex design spaces, avoid human biases, and scale up. In response, the discipline has adopted automation and artificial intelligence (AI), culminating in self-driving laboratories (SDLs), integrating high-throughput experimentation into closed-loop, AI-assisted design-build-test-learn cycles, enabling the rapid exploration of chemical spaces. However, while SDLs address throughput and complexity challenges, they introduce new forms of the original problems: algorithmic biases replace human biases, data sparsity creates constraints on design space navigation, and black-box AI models create transparency issues, complicating interpretation. These challenges emphasize a critical point: complete algorithmic autonomy is inadequate without human involvement. Human intuition, ethical judgment, and domain expertise are crucial for establishing research objectives, identifying anomalies, and ensuring adherence to ethical constraints. This perspective supports a hybrid model grounded in symbiotic autonomy, where adaptive collaboration between humans and AI enhances trust, creativity, and reproducibility. By incorporating human reasoning into adaptive AI-assisted SDL workflows, next-generation autonomous polymer discovery will be not only faster but also wiser.