{"ID":2884690,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.06591","arxiv_id":"2508.06591","title":"Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials","abstract":"Large language models (LLMs) have reshaped the research landscape by enabling new approaches to knowledge retrieval and creative ideation. Yet their application in discipline-specific experimental science, particularly in highly multi-disciplinary domains like materials science, remains limited. We present a first-of-its-kind framework that integrates generative AI with literature from hitherto-unconnected fields such as plant science, biomimetics, and materials engineering to extract insights and design experiments for materials. We focus on humidity-responsive systems such as pollen-based materials and Rhapis excelsa (broadleaf lady palm) leaves, which exhibit self-actuation and adaptive performance. Using a suite of AI tools, including a fine-tuned model (BioinspiredLLM), Retrieval-Augmented Generation (RAG), agentic systems, and a Hierarchical Sampling strategy, we extract structure-property relationships and translate them into new classes of bioinspired materials. Structured inference protocols generate and evaluate hundreds of hypotheses from a single query, surfacing novel and experimentally tractable ideas. We validate our approach through real-world implementation: LLM-generated procedures, materials designs, and mechanical predictions were tested in the laboratory, culminating in the fabrication of a novel pollen-based adhesive with tunable morphology and measured shear strength, establishing a foundation for future plant-derived adhesive design. This work demonstrates how AI-assisted ideation can drive real-world materials design and enable effective human-AI collaboration.","short_abstract":"Large language models (LLMs) have reshaped the research landscape by enabling new approaches to knowledge retrieval and creative ideation. Yet their application in discipline-specific experimental science, particularly in highly multi-disciplinary domains like materials science, remains limited. We present a first-of-i...","url_abs":"https://arxiv.org/abs/2508.06591","url_pdf":"https://arxiv.org/pdf/2508.06591v1","authors":"[\"Rachel K. Luu\",\"Jingyu Deng\",\"Mohammed Shahrudin Ibrahim\",\"Nam-Joon Cho\",\"Ming Dao\",\"Subra Suresh\",\"Markus J. Buehler\"]","published":"2025-08-08T10:41:03Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cond-mat.dis-nn\",\"cond-mat.mtrl-sci\",\"cond-mat.other\",\"cs.AI\",\"cs.CL\"]","methods":"[\"RAG\",\"Large Language Model\",\"Language Model\"]","has_code":false}