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Despite the exponential growth in computing resources and the availability ofa myriad of different theoretical water models, an accurate, yetcomputationally efficient molecular level description of mesoscopic graingrowth remains a grand challenge. The underlying phase transitions anddynamical processes in deeply supercooled systems are often renderedinaccessible due to limitations imposed by system sizes and timescales, whichis further compounded by their sluggish kinetics. Here, we introduce amachine-learned, bond-order-based potential model (BOP) that more accuratelydescribes the anomalous behavior, as well as structural and thermodynamicalproperties of both liquid water and ice, with at least two orders of magnitudecheaper computational cost than existing atomistic water models. In asignificant departure from conventional force-field fitting, we use amultilevel evolutionary strategy that trains the BOP model against not justenergetics but temperature dependent properties inferred from on-the-flymolecular dynamics simulations as well. We use the BOP model to probe thehomogeneous ice nucleation and growth process by performing molecular dynamicson multi-million molecule systems for up to microsecond time scales. Thesemassively parallel, long-time simulations naturally capture the competitionbetween cubic, hexagonal, and stacking disordered ice phases during nucleationand early stages of growth leading to the formation of nanometer sized grains.Subsequently, we elucidate the hitherto elusive mechanism of grain coarseningof the crystallized mixed ice phases, which occur through grain dissolution andOstwald ripening followed by grain boundary migration.

Ice grains grow by dissolution, ripening and grain boundary migration