Luu Rachel K, Buehler Markus J
Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA.
Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA.
Adv Sci (Weinh). 2024 Mar;11(10):e2306724. doi: 10.1002/advs.202306724. Epub 2023 Dec 25.
The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge is 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 is 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 strengthened with enhanced reasoning ability, as well as with Retrieval-Augmented Generation (RAG) to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model shows 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.
生物材料和仿生材料科学的研究已经很成熟；然而，令人惊讶的是，很少有知识被系统地转化为工程解决方案。为了加速发现并引导深入理解，本文报道了一种开源自回归Transformer大语言模型（LLM）——BioinspiredLLM。该模型使用了结构生物学和仿生材料领域一千多篇同行评议文章组成的语料库进行微调，能够被促使回忆信息、协助研究任务，并充当创造力引擎。该模型已证明能够准确回忆有关生物材料的信息，并且通过增强推理能力以及检索增强生成（RAG）在生成过程中纳入新数据得到进一步强化，这也有助于追溯来源、更新知识库以及连接知识领域。BioinspiredLLM还显示出能够针对生物材料设计提出合理假设，对于以前从未明确研究过的材料尤其如此。最后，该模型在与其他生成式人工智能模型合作的工作流程中展现出令人印象深刻的前景，这种工作流程可以重塑传统的材料设计过程。这种协作式生成人工智能方法能够刺激并增强仿生材料设计工作流程。生物材料处于多个科学领域的关键交叉点，像BioinspiredLLM这样的模型有助于连接知识领域。
