Discovering Ultra-Stable Metal–Organic Frameworks for CO₂ Capture from A Wet Flue Gas: Integrating Machine Learning and Molecular Simulation

The rapid increase in atmospheric CO₂, arising from anthropogenic sources, has posed a severe threat to global climate and raised widespread environmental concern. Metal-organic frameworks (MOFs) are promising adsorbents to potentially reduce CO₂ emissions from flue gases. However, many MOFs suffer from structural degradation and performance deterioration upon exposure to water in flue gases. Aiming to discover stable and efficient MOFs for CO₂ capture from a wet flue gas, we propose a hierarchical high-throughput computational screening (HTCS) strategy. Machine learning (ML)-assisted stability analysis is incorporated within the HTCS, leveraging prior experimental experience to predict ultrastable (including water-, thermal-, and activation-stable) MOFs from ∼280,000 candidates in the ab initio REPEAT charge MOF (ARC-MOF) database. Among 9755 shortlisted MOFs, molecular simulations identify 1000 top-performing MOFs. Remarkably, several vanadium-based MOFs are revealed to be ultrastable, exhibiting high CO₂ capture capability of 3-7 mmol/g and CO₂/N₂ selectivity of 95-401. Subsequently, ML regressors are developed to derive design principles for MOFs capable of overcoming the trade-off effect. Furthermore, an ML classifier is developed to analyze the impact of water on CO₂ capture by comparing dry and wet conditions. The proposed hierarchical HTCS and developed ML models lay a solid foundation for the potential transition of MOFs into practical applications.