  Despite the exponential growth in computing resources and the availability of
a myriad of different theoretical water models, an accurate, yet
computationally efficient molecular level description of mesoscopic grain
growth remains a grand challenge. The underlying phase transitions and
dynamical processes in deeply supercooled systems are often rendered
inaccessible due to limitations imposed by system sizes and timescales, which
is further compounded by their sluggish kinetics. Here, we introduce a
machine-learned, bond-order-based potential model (BOP) that more accurately
describes the anomalous behavior, as well as structural and thermodynamical
properties of both liquid water and ice, with at least two orders of magnitude
cheaper computational cost than existing atomistic water models. In a
significant departure from conventional force-field fitting, we use a
multilevel evolutionary strategy that trains the BOP model against not just
energetics but temperature dependent properties inferred from on-the-fly
molecular dynamics simulations as well. We use the BOP model to probe the
homogeneous ice nucleation and growth process by performing molecular dynamics
on multi-million molecule systems for up to microsecond time scales. These
massively parallel, long-time simulations naturally capture the competition
between cubic, hexagonal, and stacking disordered ice phases during nucleation
and early stages of growth leading to the formation of nanometer sized grains.
Subsequently, we elucidate the hitherto elusive mechanism of grain coarsening
of the crystallized mixed ice phases, which occur through grain dissolution and
Ostwald ripening followed by grain boundary migration.
