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

Abstract

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.