Cognitive Materials Engineering for Bioelectricity and Bioelectronics: Research and Applications

Cognitive materials engineering represents an emerging paradigm in which artificial intelligence (AI) is incorporated directly into the design, selection, and operation of bioelectrical and bioelectronic materials. Rather than relying on static material properties or fixed stimulation protocols, AI-enabled material systems can adapt their electrical, mechanical, and chemical characteristics in response to dynamic biological states. This perspective examines how machine learning, deep learning, reinforcement learning, and neuromorphic approaches can guide material chemistry, scaffold assembly, and real-time functional tuning in bioelectronic interfaces. Focusing on neural interfaces, cancer bioelectricity, wearable biosensors, and tissue engineering, we outline how adaptive materials and predictive models may enable next-generation bioelectronic platforms that learn from biological feedback and coevolve with living tissues.