A new AI-driven system has provided critical insights into how polymers can be engineered for next-generation bioelectronics, offering a powerful tool for designing materials with precise electronic properties. The research, published in the journal Matter, addresses a major challenge in developing organic electronics: the difficulty of predicting how processing methods and material structure affect a polymer’s ability to conduct electricity. Conjugated polymers, which can carry electrical charge, are essential for applications like implantable medical devices, flexible sensors, and light-harvesting systems. To enhance their conductivity, these materials are "doped" by adding a second molecule—such as F4TCNQ—to alter their electronic behavior. However, simply increasing the amount of dopant does not always improve performance. Too much can disrupt the polymer’s structure and degrade its electronic properties, making it hard to optimize without a deep understanding of the underlying relationships. To overcome this, researchers led by Aram Amassian from North Carolina State University and Baskar Ganapathysubramanian from Iowa State University developed an AI-powered experimental platform called DopeBot. This system combines high-throughput experimentation with machine learning to rapidly explore the vast space of possible processing conditions. DopeBot tested 224 variations of the polymer pBTTT doped with F4TCNQ, systematically adjusting variables like solvent type and doping temperature. After each batch of 32 experiments, the results—covering molecular structure, optical properties, and conductivity—were analyzed and fed back into the AI, which then designed the next set of experiments. This iterative process was repeated four times across three different parameter ranges. The resulting dataset provided unprecedented detail on how processing choices influence the final material’s structure and electronic performance. While the initial analysis revealed strong correlations, the team went further to establish causation. Using quantum chemical calculations, co-author Raja Ghosh from NC State identified how the precise location of dopant molecules within the polymer’s structure directly affects its conductivity. This deeper understanding enables scientists to design polymers with targeted electronic properties by controlling processing conditions. The findings are already being applied to develop new organic bioelectronic materials in collaboration with researchers from NC State, the University of Buffalo, and the Karlsruhe Institute of Technology. The work marks a significant step toward creating reliable, market-ready materials for health care and other advanced applications, moving beyond basic science to real-world innovation. The first author is Jacob Mauthe, a postdoctoral researcher at NC State, with co-authors from NC State, Iowa State, the University of North Carolina at Chapel Hill, and the University of Washington.