  Despite numerous efforts from numerical approaches to complement experimental
measurements, several fundamental challenges have still hindered one's ability
to truly provide an atomistic picture of the nucleation process in
nanocrystals. Among them, our study resolves three obstacles: (1)
Machine-learning force fields including long-range interactions able to capture
the finesse of the underlying atomic interactions, (2) Data-driven
characterization of the local ordering in a complex structural landscape
associated with several crystal polymorphs and (3) Comparing results from a
large range of temperatures using both brute-force and rare-event sampling.
Altogether, our simulation strategy has allowed us to study zinc oxide
crystallization from nano-droplet melt. Remarkably, our results show that
different nucleation pathways compete depending on the investigated degree of
supercooling.
