Reshaping MOFs Text Mining with a Dynamic Multi-Agent Framework of Large Language Agents
Zuhong Lin, Daoyuan Ren, Kai Ran, Sun Jing, Xiaotiang Huang, Haiyang He, Pengxu Pan, Xiaohang Zhang, Ying Fang, Tianying Wang, Minli Wu, Zhanglin Li, Xiaochuan Zhang, Haipu Li, Jingjing Yao
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The mining of synthesis conditions for metal-organic frameworks (MOFs) is a significant focus in materials science. However, identifying the precise synthesis conditions for specific MOFs within the vast array of possibilities presents a considerable challenge. Large Language Models (LLMs) offer a promising solution to this problem. We leveraged the capabilities of LLMs, specifically gpt-4o-mini, as core agents to integrate various MOF-related agents, including synthesis, attribute, and chemical information agents. This integration culminated in the development of MOFh6, an LLM tool designed to streamline the MOF synthesis process. MOFh6 allows users to query in multiple formats, such as submitting scientific literature, or inquiring about specific MOF codes or structural properties. The tool analyzes these queries to provide optimal synthesis conditions and generates model files for density functional theory pre modeling. We believe MOFh6 will enhance efficiency in the MOF synthesis of all researchers.