  The prototypical antiferroelectric perovskite $\rm PbZrO_3$ (PZO) has
garnered considerable attentions in recent years due to its significance in
technological applications and fundamental research. Many unresolved issues in
PZO are associated with large spatial and time scales, as well as finite
temperatures, presenting significant challenges for first-principles density
functional theory studies. Here, we introduce a deep learning interatomic
potential of PZO, enabling investigation of finite-temperature properties
through large-scale atomistic simulations. Trained using an elaborately
designed dataset, the model successfully reproduces a large number of phases,
in particular, the recently discovered 80-atom antiferroelectric $Pnam$ phase
and ferrielectric $Ima2$ phase, providing precise predictions for their
structural and dynamical properties. Using this model, we investigated phase
transitions of multiple phases, including $Pbam$/$Pnam$, $Ima2$ and $R3c$,
which show high similarity to the experimental observation. Our simulation
results also highlight the crucial role of free-energy in determining the
low-temperature phase of PZO, reconciling the apparent contradiction: $Pbam$ is
the most commonly observed phase in experiments, while theoretical calculations
predict other phases exhibiting even lower energy. Furthermore, in the
temperature range where the $Pbam$ phase is thermodynamically stable, typical
double polarization hysteresis loops for antiferroelectrics were obtained,
along with a detailed elucidation of the structural evolution during the
electric-field induced transitions between the non-polar $Pbam$ and polar $R3c$
phases.
