Accurate DDEC-charge-guided screening of high-performance metal-organic frameworks for SF₆/N₂ separation

Area of Science:

• Materials Science
• Computational Chemistry
• Chemical Engineering

Background:

• Separating sulfur hexafluoride (SF₆) from nitrogen (N₂) is crucial but challenging.
• Existing computational methods for adsorbent discovery often overlook accurate atomic charge calculations.

Purpose of the Study:

• To develop and validate a data-driven workflow for identifying high-performance metal-organic frameworks (MOFs) for SF₆/N₂ separation.
• To integrate accurate density-derived electrostatic and chemical (DDEC) partial atomic charges into grand canonical Monte Carlo (GCMC) simulations.

Main Methods:

• High-throughput computational screening of a MOF database.
• Incorporation of DDEC partial atomic charges into GCMC simulations.
• Development of a machine learning model for rapid screening.

Main Results:

• Identified top-performing MOF candidates with high SF₆ uptake and selectivity.
• Determined key structural features for efficient SF₆/N₂ separation.
• Developed a machine learning model with high predictive accuracy (R² = 0.968) for rapid screening of over 154,000 MOFs.

Conclusions:

• The DDEC-based GCMC workflow accelerates the discovery of advanced MOFs for challenging gas separations.
• Quantum-chemical datasets are vital for high-throughput material discovery.
• Zn-TCPP confirmed the workflow's reliability through density functional theory calculations.