Minisymposium Presentation

From seashells to mammal hooves to plant stems, biological materials have long captivated materials scientists and mechanical engineers due to their impressive hierarchical structure-property relationships. By understanding biological insights and motifs, the design of bio-inspired materials is empowered and poised to benefit a diverse range of applications, including sustainability. Modern generative AI frameworks, especially large language models (LLMs), show remarkable potential for science-focused applications, excelling notably in the study of biological materials through the utilization of rich legacy literature. We present BioinspiredLLM, an open-source conversational large language model that was finetuned on a corpus of biological materials literature. The model shows strong abilities in knowledge recall, creative hypothesis generation, and seamless integration into multi-agent systems. Multi-agent/agentic systems facilitate the interaction of multiple advanced AI systems, thereby expanding the scope of knowledge, enhancing data retrieval capabilities, and fostering critical thinking. This approach is demonstrated through multiple bio-inspired materials design scenarios.