(98c) Accelerating MOF Discovery for CO? Capture in Humid Conditions Via High-Throughput Screening

As global CO₂ emissions continue to rise, effective carbon capture and storage (CCS) technologies are essential for mitigating climate change. Among these, adsorption-based methods have gained attention for their potential energy efficiency and practicability. Metal–organic frameworks (MOFs) have emerged as promising adsorbents for CO₂ capture due to their tunable porosity and chemical functionality. However, for real-world carbon capture scenarios such as post-combustion flue gas, CO₂ is mostly present with N₂ and water vapor. In light of this, good CO₂ capacity, selectivity and structural stability in humid conditions are key metrics to evaluate MOF candidates. As the library of experimentally synthesized MOFs continues to grow, in this project we aim to uncover hidden gems for CO₂ capture, focusing on existing structures rather than designing new ones from scratch.

To identify top candidates from large databases, we have performed high-throughput screening (HTS), integrating molecular simulations and machine learning to rapidly evaluate over 100,000 structures. Unlike previous studies that often overlook water vapor, our workflow explicitly incorporates humid conditions to provide a more realistic and comprehensive evaluation. We not only identify top-performing MOFs with excellent CO₂ capacity and water stability but also uncover broader structure–property relationships that inform design rules for MOFs that show selective CO₂ capture under humid conditions. We highlight a representative MOF synthesized in our lab that demonstrates strong agreement with our screening.