WHTony1996/KG-RFB: Chat-RFB: a flow battery chat system leveraging knowledge graphs and large language models

The interdisciplinary nature of redox flow batteries (RFBs), spanning chemistry, materials, and engineering, has led to a vast and fragmented body of research, hindering the efficient synthesis of knowledge. An intelligent question-answering system is therefore essential to organize this dispersed knowledge, enhance information retrieval, and lower the barrier to comprehensive understanding. In this study, we leveraged the natural language processing capabilities of large language models (LLMs) and the structured nature of knowledge graphs (KGs) to establish a chat model in the field of RFBs, named Chat-RFB.