Collaborative Research: DMREF: NSF-DFG: NeuroTronics: Designer Doped Semiconductors for Neuromorphic Bioelectronics

Non-technical Description: The NeuroTronics project will create new materials that can seamlessly connect with the human nervous system, paving the way for advancements in bioelectronics. These materials, known as organic mixed ionic-electronic conductors (OMIECs), will be designed to efficiently conduct both electricity and ions and are crucial for developing improved brain-computer interfaces, therapies for neurological conditions, and more energy-efficient computing inspired by the human brain. This research could lead to breakthroughs in healthcare, human-AI interaction, computing, and robotics. The project will combine advanced computer modeling, machine learning, as well as automated and autonomous experimentation to create materials that are electronically adjustable, safe for use in the body, durable, and manufacturable at scale. A key focus will be training a new generation of scientists and engineers in AI-driven materials design through workshops and public outreach events like science museum demonstrations. By providing both fundamental knowledge and practical tools for material design, this project will overcome a major hurdle in creating reliable, mass-producible materials needed for real-world neuromorphic technologies that could eventually gain medical approval. Technical Description: This research will tackle the challenge of designing doped semiconducting polymers whose electronic properties remain stable under repeated ion insertion and mixed ionic-electronic transport. Researchers will combine sophisticated computer simulations, including density functional theory and Holstein modeling, with machine learning algorithms and automated testing systems. The work will be divided into three main areas: first, optimizing materials to achieve high carrier mobility and efficiency across different doping levels; second, designing materials that remain stable under electrochemical and thermal stress through advanced modeling and real-time monitoring; and third, developing methods for creating these materials consistently and safely using non-toxic ingredients. This integrated, closed-loop approach will accelerate development cycles and produce high-performing materials suitable for widespread deployment, directly supporting the DMREF program’s mission of revolutionizing materials innovation through data-driven, collaborative research. Next generation of materials scientists and engineers will be trained through annual workshops on FAIR data principles, AI-driven materials design, and self-driving labs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.