  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 (LLM),
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 recall information, assist with
research tasks, and function as an engine for creativity. The model has proven
that it is able to accurately recall information about biological materials and
is further enhanced with enhanced reasoning ability, as well as with
retrieval-augmented generation to incorporate new data during generation that
can also help to traceback sources, update the knowledge base, and connect
knowledge domains. 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 are at a critical intersection of multiple scientific fields and
models like BioinspiredLLM help to connect knowledge domains.
