AI Simulations Confirm Pristine Graphene Is Intrinsically Hydrophobic

For more than a decade, a fundamental mystery has surrounded graphene, the one-atom-thick “wonder material” known for its exceptional strength, conductivity, and transparency. Despite its seemingly simple structure, one basic question has remained unresolved: does graphene attract water, or repel it? The answer has proven surprisingly elusive. In some experiments, water droplets bead up on graphene, suggesting a hydrophobic (water-repellent) surface. In others, water spreads out, implying hydrophilic (water-attracting) behavior. This contradiction has fueled a long-running scientific debate and created uncertainty for applications such as desalination membranes, hydrogen fuel cells, and nanoelectronic devices, where precise control of water at interfaces is essential.

A research team led by Director CHO Minhaeng and Professor Stefan RINGE at the Center for Molecular Spectroscopy and Dynamics within the Institute for Basic Science, in collaboration with Korea University, has now resolved this puzzle. Using machine-learning–enhanced molecular simulations, the researchers demonstrate that pristine graphene is intrinsically hydrophobic and microscopically not wetting transparent.

The idea of wetting transparency had long been proposed to explain the conflicting observations. Because graphene is only one atom thick, it was thought to act like an invisible window that fails to mask the underlying substrate, thereby allowing water to 'feel' the material beneath rather than the graphene itself. If correct, this would mean that graphene does not possess a well-defined intrinsic wettability. However, directly testing this idea at the atomic scale is extremely challenging. Conventional measurements, such as water contact angles, provide only macroscopic information and cannot reveal how individual water molecules behave at the interface.

To overcome this limitation, the IBS-led team constructed what is effectively a high-precision “digital laboratory.” By developing machine-learning interatomic potentials trained on quantum-chemical calculations, they achieved near first-principles accuracy while dramatically accelerating simulations. This approach enabled them to model the structure and vibrational behavior of water at graphene interfaces in unprecedented detail.

The simulations revealed a clear microscopic picture. Water molecules near graphene adopt configurations characteristic of hydrophobic surfaces, including so-called dangling O–H bonds, in which one hydrogen atom points toward the graphene without forming a hydrogen bond. These features become even more pronounced as the number of graphene layers increases, indicating that thicker graphene is even more strongly hydrophobic.

The key to resolving the long-standing contradiction lies in intercalated water—water molecules trapped in the nanoscopic gap between graphene and its supporting substrate. For monolayer graphene placed on a hydrophilic substrate, water can readily slip beneath the graphene layer. These hidden water molecules form a confined interfacial layer with a distinct molecular structure. When experimental techniques such as vibrational sum-frequency generation (vSFG) spectroscopy probe the system, they detect signals from both the water above the graphene and the water trapped below it. Crucially, these signals can partially cancel each other.

The study also identifies a clear thickness-dependent transition. While water intercalation is thermodynamically favorable for monolayer graphene, it becomes increasingly unfavorable as additional layers are added. For multilayer graphene, the energetic cost of trapping water beneath the material becomes prohibitively high, effectively preventing intercalation. As a result, thicker graphene reveals its true behavior as a robust, water-repellent barrier, while monolayer graphene can appear hydrophilic under certain experimental conditions due to hidden interfacial water. This provides the first unified explanation for why single-layer and multi-layer graphene have seemed to behave so differently in previous studies.

Beyond resolving a fundamental scientific question, the findings have important practical implications. Many graphene-based technologies rely on controlling water at interfaces, including nanofluidic devices, desalination membranes, energy storage systems, and hydrogen fuel cells. The study shows that even a thin, hidden layer of water can significantly alter interfacial behavior, meaning that unintended water intercalation must be carefully considered in device design.

The work also highlights the importance of experimental conditions. Even if graphene is initially cleaned or annealed, water can re-enter beneath monolayer graphene during measurement due to capillary forces. Preventing such ingress - for example, by sealing graphene edges - may be necessary to accurately probe intrinsic properties.

More broadly, the study demonstrates how machine-learning–enhanced simulations can resolve complex interfacial phenomena that are difficult to disentangle experimentally. By bridging the gap between quantum accuracy and large-scale modeling, the researchers were able to isolate the roles of substrate effects, graphene thickness, and confined water.

The study was published in Nature Communications in April 2, 2026.

Read the original article on the Institute for Basic Science.