  Aided by a neural network representation of the density functional theory
(DFT) potential energy landscape of water in the RPBE approximation corrected
for dispersion, we calculate several structural and thermodynamic properties of
its liquid/vapor interface. The neural network speed allows us to bridge the
size and time scale gaps required to sample the properties of water along its
liquid/vapor coexistence line with unprecedented precision.
