Conversational Large-Language-Model Artificial Intelligence Agent for Accelerated Synthesis of Metal-Organic...

Metal-organic frameworks (MOFs) attract significant attention for their structural diversity and design flexibility, making them ideal candidates for catalytic applications. However, the traditional trial-and-error approach for optimizing MOF synthesis remains inefficient. In this study, we introduce the MOFsyn agent, an AI-driven framework that harnesses large language models (LLMs) for MOF synthesis optimization. This system integrates data automatic analysis, material mechanism analysis, and experimental protocol navigation by employing retrieval-augmented generation (RAG) to refine synthetic strategies based on natural language inputs. Using Ni@UiO-66(Ce) for olefin hydrogenation as a case study, the MOFsyn agent analyzed the relationship between synthesis conditions, structural characteristics, and catalytic performance, with a particular focus on the electronic structure of nickel. Through adaptive optimization, a novel stepwise reduction strategy was proposed that outperformed conventional one-pot reduction. The optimized Ni@UiO-66(Ce)-R2T1, synthesized under MOFsyn agent's guidance, exhibited nearly twice the Ni⁰/Ni^total ratio compared to the best-performing sample from an initial experimental set and achieved 100% conversion and selectivity for dicyclopentadiene hydrogenation under mild conditions (70 °C, 2 MPa). These results validate the accuracy and efficiency of the MOFsyn agent. This study provides an efficient tool for intelligent material synthesis, enabling researchers without programming expertise to accelerate material development.