The air-water interface forms a unique heterogeneous environment distinctly different from the bulk phase. Characterizing CO 2 behavior at these dynamically evolving interfaces requires understanding how interfacial water structure and dynamics influence CO 2 adsorption, desorption, diffusion, and reaction rates. The nanoscale region between air and water governs the flux at which CO 2 transfers into the aqueous phase, where it subsequently interacts with absorbent molecules. The flux is indirectly affected by the anisotropy of intermolecular interactions between water and CO 2 within the instantaneous water surface, dictating the structuring and dynamics of CO 2 at this boundary. Previous studies of CO 2 at the aqueous interface have largely relied on ensemble-averaged descriptions, such as interfacial tension, width, and molecular orientation from such tech-niques as Vibrational Sum-Frequency Generation spectroscopy (VSFG), Second Harmonic Generation (SHG), or X-ray spectroscopy. However, the role of interfacial barriers and solvent effects on CO 2 transport into the aqueous phase remains poorly understood. Classical models can enable the study of long timescale behavior but often lack accuracy, while ab initio molecular dynamics (AIMD) can provide a more accurate description, but limited to short timescales due to high computational expense. To address this gap, we developed neural network potentials to describe the bulk and interfacial water in the presence of CO 2 trained on data from AIMD simulations performed using the revPBE density functional with the DFT-D3 dispersion correction. This choice of the DFT method provides a good balance between the accuracy and computational cost, given its superior performance in capturing both bulk and interfacial properties among the family of generalized gradient approximation (GGA) methods. Although more advanced ab initio methods could enhance the quality of the NNP potentials, the required timescales and system sizes to develop such potentials and accurately capture interfacial properties remain difficult to achieve, even with current computational capabilities. Using neural network potentials, we aimed to improve the accuracy of classical potentials to describe the structure and dynamics of CO 2 in the interfacial region. This work characterizes the interfacial chemical processes controlling the transport of CO 2 through the interfacial phase boundary. Understanding these processes is crucial to develop a complete understanding of the direct air capture of CO 2 at the air/water interface and explain why CO 2 reactions with amino acids are kinetically accelerated at these interfaces, as revealed by our recent study. Herein, we find that the free energy of CO 2 transport from the air to the aqueous phase is influenced by the distribution or the spread of instantaneous water molecules, creating a unique coordination environment for interfacial CO 2 . In this study, extensive AIMD simulations were carried out on 3 bulk and 4 air/water interface systems. Each system was simulated for at least 40 ps, with a total of over 280 ps of AIMD simulations. From these simulations, 280,000 snapshots were extracted and used to train the NNP models, with details provided in the SI. We then employed the trained neural network potentials in LAMMPS patched with DeepMD-kit to simulate air-water systems with two interfaces, initially placing CO 2 randomly in the simulation cell at a partial pressure of about 0.145 atm. This pressure, which is relevant for direct air capture, is sufficiently low to accurately compute interfacial properties while minimizing CO 2 clustering and bubble formation observed at higher concentrations. We performed additional simulations doubling the CO 2 concentration and observed a similar potential of mean force (PMF) for CO 2 along the z axis (Figure ). This indicates that at the studied concentrations, the PMFs remains almost unaffected, validating our system for investigating structure and dynamics under DAC conditions. However, further increases in CO 2 concentration would begin to impact interfacial properties such as interfacial tension and width. The analysis of the interfacial systems by Niblett et al. suggests that the results for certain properties such as water density distribution and orientation remain largely unaffected when explicitly modeled long-range effects are introduced. Recent studies showed that a comprehensive training on both bulk and interfacial systems can provide more accurate description of the interface, where the symmetry of the intermolecular interactions is broken and the long-range interactions are anisotropic. In this study, we followed this strategy of extensive training, ensuring that both the bulk and interfacial results are comparable. Importantly, in the systems without ions present at the interface, it is indeed possible to achieve accurate results without a significant increase in error for interfacial systems. The RMSE in energy and forces are obtained to be < 1 meV/atom and < 100 meV/Å respectively for both bulk and interface (Table ), showing similar errors for trained NNP model. In cases where ions are introduced, explicit treatment of long-range electrostatics may be required to capture the correct behavior. Additional technical details are provided in the SI.