Accelerating CO₂ Direct Air Capture Screening for Metal-Organic Frameworks with a Transferable Machine Learning Force Field

Direct air capture (DAC) of CO₂ is necessary for climate change mitigation, but it faces challenges from low atmospheric CO₂ concentrations and competition from water vapor. Metal-organic frameworks (MOFs) are attractive candidates for DAC owing to their exceptionally high surface area, tunable porosity, and potential for adsorption-based capture processes with relatively low regeneration cost. Identifying optimal MOFs is hindered by their structural complexity, the vastness of their chemical space, and the expense of accurate simulations. Here, we present a machine learning force field (MACE-DAC) tailored for CO2 and H2O interactions in MOFs by finetuning the foundation model MACE-MP-0. To address smoothing issues and catastrophic forgetting, we curated the diverse GoldDAC dataset and introduced a continual learning loss function. To efficiently sample gas configurations, we developed the DAC-SIM package that uses MLFFs to achieve ab initio quality thermodynamics based on Widom insertion at computational speeds comparable to classical force fields. High-throughput screening on more than 8,000 synthesized MOF structures was performed to identify optimal MOFs and extract important chemical features. This approach overcomes prior limitations in describing CO2/MOF and H₂O/MOF interactions, providing a scalable and accurate framework for accelerating DAC research for porous materials.