A Conversational Large-Language-Model AI Agent for Synthesis of Metal-Organic Frameworks for Efficient Hydrogenation of Dicyclopentadiene

Metal-Organic Frameworks (MOFs), renowned for their structural diversity and design flexibility, have demonstrated considerable potential in catalysis. However, the traditional trial-and-error approach is inefficient for optimizing catalytic performance in synthetic space. In this work, we propose MOFsyn Agent, a framework leveraging Large Language Models (LLMs) to guide the efficient synthesis and performance optimization of MOFs. MOFsyn Agent is an integrated system comprising two core components: data automatic analyzer and material mechanism analyzer. The system can be driven to perform data analysis operations such as task disassembly, code writing and execution by simple natural language commands only. Furthermore, the incorporation of the Retrieval-Augmented Generation (RAG) technology enables MOFsyn Agent to access external knowledge bases in real time, significantly enhancing its domain applicability in MOFs synthesis. Taking Ni@UiO-66(Ce) as an illustrative example, MOFsyn Agent conducted a comprehensive analysis of the relationships between synthetic conditions, structural characteristics, and catalytic performance. Through machine learning (ML), key features such as nickel content and metallic nickel were identified. Notably, MOFsyn Agent not only realizes the numerical prediction based on ML, but also optimizes the synthesis scheme from the textual level, and creatively proposes a step-by-step reduction strategy, which replaces the traditional one-pot direct reduction method. Experimental results demonstrated that the optimized Ni@UiO-66(Ce) can achieve saturated hydrogenation of dicyclopentadiene under more mild conditions (80 °C, 2 MPa) in only 1 h, validating the accuracy and reliability of the proposed method. This research provides an efficient tool for material intelligent synthesis to researchers without a programming background, accelerating the development process of new materials.