Toward smart CO2 capture by the synthesis of metal organic frameworks using large language models

Abstract

This research focuses on efficiently collecting CO₂ adsorption data using experimental metal–organic framework (MOF) porous materials from the scientific literature, addressing the challenges related to data classification and access to MOF synthesis methods. The aim is to organize, classify, and facilitate easy access to materials science information using artificial intelligence (AI). Using advanced large language models (LLMs), we developed a systematic approach to extract and sort MOF synthesis data for CO₂ adsorption in a structured format. Using this method, we collected data from over 433 published experimental research papers and created a specific dataset to analyze the effects of metals, ligands, and carbon adsorption conditions on CO₂ uptake performance. The correlations between the material structure, such as metal types, ligands, specific surface area, pore size, pore volume, synthesis conditions, and CO₂ adsorption, under various process conditions were examined using the final database. We applied ChatGPT 4o mini as an AI assistant to text-mine all MOF information from different PDF file references. In addition to revealing the impact of each parameter on CO₂ uptake and MOF structure before synthesis, the AI analysis findings indicated which ligand and metal groups should be altered to customize the MOF structure for improved CO₂ capture.