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Experimental measurements have reported ultra-fast and radius-dependent watertransport in carbon nanotubes which are absent in boron nitride nanotubes.Despite considerable effort, the origin of this contrasting (and fascinating)behaviour is not understood. Here, with the aid of machine learning-basedmolecular dynamics simulations that deliver first-principles accuracy, weinvestigate water transport in single-wall carbon and boron nitride nanotubes.Our simulations reveal a large, radius-dependent hydrodynamic slippage on bothmaterials with water experiencing indeed a $\approx 5$ times lower friction oncarbon surfaces compared to boron nitride. Analysis of the diffusion mechanismsacross the two materials reveals that the fast water transport on carbon isgoverned by facile oxygen motion, whereas the higher friction on boron nitridearises from specific hydrogen-nitrogen interactions. This work not onlydelivers a clear reference of unprecedented accuracy for water flow insingle-wall nanotubes, but also provides detailed mechanistic insight into itsradius and material dependence for future technological application.

Water flow in single-wall nanotubes: Oxygen makes it slip, hydrogen makes it stick