(556e) Integrating Stability with High-Throughput Screening of Metal-Organic Frameworks for CO2 Capture in Wet Flue Gas: A Combination of Machine Learning and Molecular Simulation

Metal-organic frameworks (MOFs), as emerging sorbents for CO₂ capture, face the challenging stability issue in the presence of water. Aiming to accelerate the discovery of stable and efficient MOFs for CO₂ capture from a wet flue gas, we propose a high-throughput computational screening (HTCS) strategy by integrating three stability metrics with separation performance assessment. The water, thermal and activation stabilities of MOFs are predicted using machine learning models, while separation performance is evaluated through grand canonical Monte Carlo simulations. The integrated HTCS strategy is applied to screen ~280,000 candidates within the ab initio REPEAT charge MOF (ARC−MOF) database. We identify stable MOFs exhibiting CO₂/N₂ selectivity > 50 and CO₂ working capacity > 4 mmol/g in the presence of water. Quantitative structure-stability and structure-performance relationships are simultaneously established to derive design principles for stable and efficient MOFs against competitive co-adsorption and attack from water. This integrated HTCS paves the way to develop new MOFs, accelerating their translation into practical applications.