Mining Solid-State Electrolytes from Metal–Organic Framework Databases through Large Language Models and Representation Clustering

Metal-organic frameworks (MOFs) are attracting increasing attention as solid-state electrolytes (SSEs) due to their three-dimensional porous diffusion paths for Li⁺ migration. However, their development is hindered by their inherent complexity and the absence of design guidelines. Large language models (LLMs) and machine learning, as emerging artificial intelligence (AI) technologies, can significantly accelerate the development of MOF SSEs by analyzing data and identifying potential materials. Herein, we use LLMs and representation clustering to intelligently mine MOF SSEs from 11,393 candidate MOF materials, subsequently verified by physicochemical characterizations and electrochemical demonstration. Specifically, we adopt an interactive iteration text mining framework based on LLMs to extract information on MOF SSEs, constructing a specialized data set for structural and electrochemical properties of MOF SSEs, with high precision and recall. Each property is projected into a representation space, and representation clustering is performed on samples to mine promising MOF SSEs from a candidate MOF data set. As a typical result, NOTT-400 is successfully identified through the clustering analysis, exhibiting high Li⁺ conductivity (2.23 × 10^-4 S cm^-1) and a wide electrochemical stability window (0-4.79 V), confirming both the material feasibility and the reliability of the entire AI-driven approach. The AI-assisted mining of novel MOF SSEs, along with their design principles, creates a new paradigm for accelerating materials discovery.