Water
glasses have attracted considerable attention due to their
potential connection to a liquid–liquid transition in supercooled
water. Here we use molecular simulations to investigate the formation
and phase behavior of water glasses using the machine-learned bond-order
parameter (ML-BOP) water model. We produce glasses through hyperquenching
of water, pressure-induced amorphization (PIA) of ice, and pressure-induced
polyamorphic transformations. We find that PIA of polycrystalline
ice occurs at a lower pressure than that of monocrystalline ice and
through a different mechanism. The temperature dependence of the amorphization
pressure of polycrystalline ice for ML-BOP agrees with that in experiments.
We also find that ML-BOP accurately reproduces the density, coordination
number, and structural features of low-density (LDA), high-density
(HDA), and very high-density (VHDA) amorphous water glasses. ML-BOP
accurately reproduces the experimental radial distribution function
of LDA but overpredicts the minimum between the first two shells in
high-density glasses. We examine the kinetics and mechanism of the
transformation between low-density and high-density glasses and find
that the sharp nature of these transitions in ML-BOP is similar to
that in experiments and all-atom water models with a liquid–liquid
transition. Transitions between ML-BOP glasses occur through a spinodal-like
mechanism, similar to ice crystallization from LDA. Both glass-to-glass
and glass-to-ice transformations have Avrami–Kolmogorov kinetics
with exponent n = 1.5 ± 0.2 in experiments and
simulations. Importantly, ML-BOP reproduces the competition between
crystallization and HDA→LDA transition above the glass transition
temperature T
g, and separation of their
time scales below T
g, observed also in
experiments. These findings demonstrate the ability of ML-BOP to accurately
reproduce water properties across various regimes, making it a promising
model for addressing the competition between polyamorphic transitions
and crystallization in water and solutions.