Gäding, JohannesJohannesGädingDella Balda, VincenteVincenteDella BaldaLan, JinggangJinggangLanKonrad, JulianJulianKonradIannuzzi, MarcellaMarcellaIannuzziMeißner, RobertRobertMeißnerTocci, GabrieleGabrieleTocci2024-09-252024-09-252024-11-17Proceedings of the National Academy of Sciences of the United States of America 121 (28): e2407877121 (2024-09-17)https://hdl.handle.net/11420/49186Understanding the structure in the nanoscopic region of water that is in direct contact with solid surfaces, so-called contact layer, is key to quantifying macroscopic properties that are of interest to e.g. catalysis, ice nucleation, nanofluidics, gas adsorption, and sensing. We explore the structure of the water contact layer on various technologically relevant solid surfaces, namely graphene, MoS[Formula: see text], Au(111), Au(100), Pt(111), and Pt(100), which have been previously hampered by time and length scale limitations of ab initio approaches or force field inaccuracies, by means of molecular dynamics simulations based on ab initio machine learning potentials built using an active learning scheme. Our results reveal that the in-plane intermolecular correlations of the water contact layer vary greatly among different systems: Whereas the contact layer on graphene and on Au(111) is predominantly homogeneous and isotropic, it is inhomogeneous and anisotropic on MoS[Formula: see text], on Au(100), and on the Pt surfaces, where it additionally forms two distinct sublayers. We apply hydrodynamics and the theory of the hydrophobic effect, to relate the energy corrugation and the characteristic length-scales of the contact layer with wetting, slippage, the hydration of small hydrophobic solutes and diffusio-osmotic transport. Thus, this work provides a microscopic picture of the water contact layer and links it to macroscopic properties of liquid/solid interfaces that are measured experimentally and that are relevant to wetting, hydrophobic solvation, nanofluidics, and osmotic transport.en1091-6490202438National Acad. of Scienceshttps://creativecommons.org/licenses/by/4.0/aqueous interfacesmachine learningmolecular dynamicsnanofluidicsMLE@TUHHNatural Sciences and Mathematics::530: PhysicsNatural Sciences and Mathematics::541: Physical; TheoreticalTechnology::621: Applied PhysicsJournal Article10.15480/882.1332210.1073/pnas.240787712110.15480/882.13322Journal Article