  Water freezing is ubiquitous on Earth, affecting many areas from biology to
climate science and aviation technology. Probing the atomic structure in the
homogeneous ice nucleation process from scratch is of great value but still
experimentally unachievable. Theoretical simulations have found that ice
originates from the low-mobile region with increasing abundance and persistence
of tetrahedrally coordinated water molecules. However, a detailed microscopic
picture of how the disordered hydrogen-bond network rearranges itself into an
ordered network is still unclear. In this work, we use a deep neural network
(DNN) model to "learn" the interatomic potential energy from quantum mechanical
data, thereby allowing for large-scale and long molecular dynamics (MD)
simulations with ab initio accuracy. The nucleation mechanism and dynamics at
atomic resolution, represented by a total of 36 $\mu$s-long MD trajectories,
are deeply affected by the structural and dynamical heterogeneity in
supercooled water. We find that imperfectly coordinated (IC) water molecules
with high mobility pave the way for hydrogen-bond network rearrangement,
leading to the growth or shrinkage of the ice nucleus. The hydrogen-bond
network formed by perfectly coordinated (PC) molecules stabilizes the nucleus,
thus preventing it from vanishing and growing. Consequently, ice is born
through competition and cooperation between IC and PC molecules. We anticipate
that our picture of the microscopic mechanism of ice nucleation will provide
new insights into many properties of water and other relevant materials.
