Intelligent, Personalized Scientific Assistant via Large Language Models for Solid-State Battery Research

Abstract

In response to the rapid advancements and heightened competition within solid-state battery research, the sheer volume of publications presents a significant challenge for researchers seeking comprehensive insights. This paper introduces ChatSSB, an advanced research assistant designed to bolster scientific inquiry within this dynamic field. Leveraging the Retrieval-Augmented Generation (RAG) framework, ChatSSB excels in extracting precise information from the latest research publications through an intuitive Q&A interface. Beyond its foundational capabilities, ChatSSB boasts a customizable expert knowledge database, continuously updated through a dynamic feedback mechanism. This ensures researchers have access to cutting-edge and reliable information, overcoming the limitations of outdated or incomplete literature. Furthermore, the integration of multiagent collaboration and embedded tools within RAG facilitates robust quantitative analysis, enabling efficient data collection, visualization, and interpretation. Collectively, these features empower ChatSSB to deliver precise, actionable insights, significantly accelerating innovation in solid-state battery technology and propelling it toward the next frontier of materials science.