Accelerating CO2 direct air capture screening for metal-organic frameworks with a transferable machine learning force field

Abstract

Direct air capture (DAC) of CO2 is necessary for climate change mitigation, but it faces challenges from low CO2 concentrations and competition from water vapor. Metal-organic frameworks (MOFs) hold significant promise for DAC owing to their high surface area and adsorption-based capture processes. However, identifying optimal MOFs is hindered by structural complexity and vast chemical diversity. Here, we introduced a machine learning force field (MLFF) tailored for CO2 and H2O interactions in MOFs by fine-tuning a foundation model. To address smoothing issues and catastrophic forgetting, we curated the GoldDAC dataset and introduced a continual learning scheme. We further developed DAC-SIM, a molecular simulation package integrated with MLFF, including a Widom insertion. Then, we screened an extensive MOF database, uncovering high-performing MOFs and identifying chemical features for DAC applications. This approach overcomes prior limitations in describing MOF-CO2 and MOF-H2O interactions, providing a scalable and accurate framework for DAC research of porous materials.