Identification of Metal–Organic Frameworks for CO2 Capture from Humid Flue Gas: Integrating Molecular Simulation, Machine Learning, and Experimental Synthesis

Increasing global CO2 emissions are driving efforts to develop advanced carbon capture materials. Metal-organic frameworks (MOFs) show great promise as CO2 adsorbents, yet maintaining performance under humid flue gas conditions remains a major challenge. Herein, we present an integrated computational high-throughput screening workflow that begins with a library of over 110,000 experimental MOFs or MOF-like structures, explicitly includes the effects of water in adsorption simulations, and incorporates machine learning-based stability analysis to identify promising candidates from among existing MOFs. Guided by this workflow, we synthesized a top-performing MOF that demonstrates exceptional tolerance to humid conditions, maintaining high CO2 uptake at elevated relative humidity. We further revealed key structure-property relationships and structural motifs that offer valuable design principles for next-generation MOFs for CO2 capture in humid conditions.