Python-Based High-Throughput Extraction of Void Information, Solvent Accessible Volume and Adsorbate Molecules from MOF for Adsorption-Separation Applications

With the rapid growth of chemical data and information, there is an increasing need for chemistry undergraduates to master Python tools for analyzing large chemical datasets and extracting key or feature information. Currently, more than 100,000 types of metal-organic frameworks (MOFs), as the material recently awarded the Nobel Prize in Chemistry, have been experimentally synthesized. The performance of MOFs in adsorption-separation applications depends on their specific void characteristics, including void count, spatial distribution, and volume size. This study presents the entire process including data collection, recognition of key or feature information, the workflow of using Python tools, and the automatic output of results for void information, solvent accessible volume (SAV) and adsorbate molecules. By processing 219 CIF files collected from open-access publications, CCDC, and supporting information files, we successfully extracted 498 total blocks, including 259 blocks with void information, 157 blocks with SAV data, 286 blocks with squeeze details, and 1,573 individual voids. In addition, we identified adsorbate molecules (diethyl ether, chloroform, water, ethanol, toluene, carbon dioxide) in MOFs. This hands-on approach can help students understand the complete workflow of Python-based big data processing, gradually acquiring essential skills for handling big chemical datasets. This educational framework can be effectively integrated into undergraduate scientific writing courses, enhancing chemistry majors' capabilities in large data processing and preparing them for future chemical education and research challenges. The method demonstrates computational efficiency, requiring only standard CPU resources to rapidly process large datasets.