Conversational Large-Language-Model Artificial Intelligence Agent for Accelerated Synthesis of Metal–Organic Frameworks Catalysts in Olefin Hydrogenation

Abstract

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.