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The rapid increase in atmospheric CO 2, 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 CO 2 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 CO 2 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 CO 2 capture capability of 3–7 mmol/g and CO 2 /N 2 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 CO 2 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.
Zhang et al. (Fri,) studied this question.