Python-based high-throughput extraction of void information, solvent accessible volume and adsorbate molecules from...

Area of Science:

• Materials Science
• Computational Chemistry
• Chemical Engineering

Background:

• The increasing volume of chemical data necessitates efficient analysis tools.
• Metal-organic frameworks (MOFs) are crucial for adsorption-separation applications, with performance linked to void characteristics.
• Over 100,000 MOF types have been synthesized, highlighting the need for automated data processing.

Purpose of the Study:

• To develop and present a Python-based workflow for automated extraction of key information from MOF datasets.
• To analyze void characteristics (count, distribution, size), solvent accessible volume (SAV), and identify adsorbate molecules within MOFs.
• To demonstrate the computational efficiency of the developed method for large-scale chemical data analysis.

Main Methods:

• Data collection from open-access publications, CCDC, and supporting information files (219 CIF files processed).
• Utilized Python tools for recognition of key information, including void details, SAV, and adsorbate molecules.
• Automated workflow for processing MOF crystal structure data and generating results.

Main Results:

• Successfully processed 219 CIF files, extracting 498 total blocks.
• Identified 259 blocks with void information, 157 with SAV data, and 286 with squeeze details, totaling 1573 individual voids.
• Identified common adsorbate molecules like diethyl ether, chloroform, water, ethanol, toluene, and carbon dioxide within MOFs.

Conclusions:

• The developed Python workflow provides an efficient and computationally inexpensive method for analyzing large MOF datasets.
• Automated extraction of void characteristics and adsorbate information is feasible and valuable for understanding MOF performance.
• This approach supports the advancement of MOF research and applications in adsorption-separation technologies.