  Observing non-classical nucleation pathways remains challenging in
simulations of complex materials with technological interests. This is because
it requires very accurate force fields that can capture the whole complexity of
their underlying interatomic interactions and an advanced structural analysis.
Here, we first report the construction of a machine-learning force field for
zinc oxide interactions using the Physical LassoLars Interaction Potentials
approach which allows us to be predictive even for untrained structures. Then,
we carried out freezing simulations from a liquid and observed the crystal
formation with atomistic precision. Our results, which are analyzed using a
data-driven approach based on bond order parameters, demonstrate the presence
of both prenucleation clusters and two-step nucleation scenarios thus
retrieving seminal predictions of non-classical nucleation pathways made on
much simpler models.
