Aqueous solutions containing amine-based molecules is a promising class of absorbents for direct air capture (DAC) of CO 2 . For CO 2 to traverse the gas/liquid boundary, it must pass through the interface. Although interfaces represent only a minor fraction of the total solvent molecules, they serve as crucial bottlenecks for controlling CO 2 transport and reactivity. A detailed molecular picture of how CO 2 moves from air into the aqueous phase and interacts with water and absorbents in the interfacial region could reveal valuable strategies to accelerate DAC. A complete understanding of the CO 2 capture mechanism requires investigating CO 2 behavior at both neat and tailored interfaces. This study aims to explore CO 2 behavior at the pure air-water interface using machine learning interatomic potentials trained based on density functional theory (DFT) calculations and contrasts them with the results from classical simulations that lack the explicit account of polarization effects.