Harnessing Large Language Models to Collect and Analyze Metal-Organic Framework Property Data Set.

This research focused on the efficient collection of experimental metal-organic framework (MOF) data from scientific literature to address the challenges of accessing hard-to-find data and improving the quality of information available for machine learning studies in materials science. Utilizing a chain of advanced large language models (LLMs), we developed a systematic approach to extract and organize MOF data into a structured format. Our methodology successfully compiled information from more than 40,000 research articles, creating a comprehensive and ready-to-use data set. Specifically, data regarding MOF synthesis conditions and properties were extracted from both tables and text and then analyzed. Subsequently, we utilized the curated database to analyze the relationships between synthesis conditions, properties, and structure. Through machine learning, we identified the existence of a gap between simulation data and experimental data, and further analysis revealed the factors contributing to this discrepancy. Additionally, we leveraged the extracted synthesis condition data to develop a synthesis condition recommender system. This system suggests optimal synthesis conditions based on the provided precursors, offering a practical tool to refine synthesis strategies. This underscores the importance of experimental datasets in advancing MOF research.