Biomimetic materials mimic biological structures and functions. They are crucial for addressing complex challenges in tissue engineering, sustainable architecture, and energy storage. Traditionally, designing these materials requires slow, resource-intensive trial-and-error methods and physics-based simulations. Recently, Artificial Intelligence (AI) and Machine Learning (ML) have transformed this field. They translate biological intelligence into actionable engineering logic and rapidly explore massive design spaces. Despite rapid advancements, the field still faces several critical bottlenecks, including complexity mismatches, data scarcity, and limited interpretability. This review examines AI-driven biomimetic design across five primary “interfaces”: (1) Biological Pattern Recognition, (2) Structural Optimization, (3) Generative Morphogenesis, (4) Adaptive Fabrication, and (5) Data-Driven Discovery Platforms. The review also outlines future perspectives, especially the shift toward autonomous “closed-loop” laboratories. In these labs, AI will manage the entire workflow, i.e., design, synthesis, and testing, without human intervention. Future efforts will likely focus on multi-model data mining to understand complex, life-like properties. Furthermore, research aims to develop Explainable AI (XAI) to ensure deterministic modeling in safety-critical applications. The ultimate goal is a synergistic relationship. AI will design materials, but these materials, using biomimetic metabolic or neural models, will also help construct more efficient AI architectures.
Hu et al. (Sun,) studied this question.
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