The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge has been systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model, BioinspiredLLM, is reported. The model was finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to actively and interactively recall information, assist with research tasks, and function as an engine for creativity. The model has proven by example that it is not only able to accurately recall information about biological materials when queried but also formulate biomaterials questions and answers that can evaluate its own performance. BioinspiredLLM also has been shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model showed impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials is at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains.

通过自动回归变换器大型语言模型 BioinspiredLLM，我们可以加速发现并指导研究，通过该模型可以提供信息回忆、协助研究任务和推动创造力等功能，同时它还能与其他生成式人工智能模型协作，重塑传统材料设计流程。

生物和仿生材料力学的对话式大型语言模型BioinspiredLLM