  Experimental measurements have reported ultra-fast and radius-dependent water
transport 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-based
molecular dynamics simulations that deliver first-principles accuracy, we
investigate water transport in single-wall carbon and boron nitride nanotubes.
Our simulations reveal a large, radius-dependent hydrodynamic slippage on both
materials with water experiencing indeed a $\approx 5$ times lower friction on
carbon surfaces compared to boron nitride. Analysis of the diffusion mechanisms
across the two materials reveals that the fast water transport on carbon is
governed by facile oxygen motion, whereas the higher friction on boron nitride
arises from specific hydrogen-nitrogen interactions. This work not only
delivers a clear reference of unprecedented accuracy for water flow in
single-wall nanotubes, but also provides detailed mechanistic insight into its
radius and material dependence for future technological application.
