  Computational studies of liquid water and its phase transition into vapor
have traditionally been performed using classical water models. Here we utilize
the Deep Potential methodology -- a machine learning approach -- to study this
ubiquitous phase transition, starting from the phase diagram in the
liquid-vapor coexistence regime. The machine learning model is trained on ab
initio energies and forces based on the SCAN density functional which has been
previously shown to reproduce solid phases and other properties of water. Here,
we compute the surface tension, saturation pressure and enthalpy of
vaporization for a range of temperatures spanning from 300 to 600 K, and
evaluate the Deep Potential model performance against experimental results and
the semi-empirical TIP4P/2005 classical model. Moreover, by employing the
seeding technique, we evaluate the free energy barrier and nucleation rate at
negative pressures for the isotherm of 296.4 K. We find that the nucleation
rates obtained from the Deep Potential model deviate from those computed for
the TIP4P/2005 water model, due to an underestimation in the surface tension
from the Deep Potential model. From analysis of the seeding simulations, we
also evaluate the Tolman length for the Deep Potential water model, which is
(0.091 $\pm$ 0.008) nm at 296.4 K. Lastly, we identify that water molecules
display a preferential orientation in the liquid-vapor interface, in which H
atoms tend to point towards the vapor phase to maximize the enthalpic gain of
interfacial molecules. We find that this behaviour is more pronounced for
planar interfaces than for the curved interfaces in bubbles. This work
represents the first application of Deep Potential models to the study of
liquid-vapor coexistence and water cavitation.
