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

Abstract

Achieving high SF₆ uptake and SF₆/N₂ selectivity is a key challenge in gas separation. High-throughput computational screening is an efficient strategy to identify high-performing adsorbents. However, these candidates may be overlooked because most studies rely on empirical partial charge assignments. In this study, we present a data-driven workflow that integrates accurate density-derived electrostatic and chemical (DDEC) partial atomic charges into grand canonical Monte Carlo (GCMC) simulations to accelerate the discovery of high-performance MOFs for SF₆/N₂ separation. By screening the quantum-chemical metal–organic framework (MOF) database, several top-performing candidates with high SF₆ uptake and selectivity were identified. The key features for efficient separation were open metal sites, parallel aromatic surfaces, uncoordinated nitrogen atoms, and metal–oxygen–metal bridges. A machine learning model trained on the DDEC-based GCMC results achieved excellent predictive performance (coefficient of determination = 0.968, mean absolute error = 0.281 mmol g⁻¹) and enabled rapid screening of 154 144 MOFs within 50 min. Zn-TCPP was selected for validation via density functional theory calculations, confirming the reliability of the proposed workflow. This study illustrates how quantum-chemical datasets facilitate high-throughput material discovery for challenging separations.