  The discovery of two-dimensional (2D) ferroelectrics with switchable
out-of-plane polarization such as monolayer $\alpha$-In$_2$Se$_3$ offers a new
avenue for ultrathin high-density ferroelectric-based nanoelectronics such as
ferroelectric field effect transistors and memristors. The functionality of
ferroelectrics depends critically on the dynamics of polarization switching in
response to an external electric/stress field. Unlike the switching dynamics in
bulk ferroelectrics that have been extensively studied, the mechanisms and
dynamics of polarization switching in 2D remain largely unexplored. Molecular
dynamics (MD) using classical force fields is a reliable and efficient method
for large-scale simulations of dynamical processes with atomic resolution. Here
we developed a deep neural network-based force field of monolayer In$_2$Se$_3$
using a concurrent learning procedure that efficiently updates the
first-principles-based training database. The model potential has accuracy
comparable with density functional theory (DFT), capable of predicting a range
of thermodynamic properties of In$_2$Se$_3$ polymorphs and lattice dynamics of
ferroelectric In$_2$Se$_3$. Pertinent to the switching dynamics, the model
potential also reproduces the DFT kinetic pathways of polarization reversal and
180$^\circ$ domain wall motions. Moreover, isobaric-isothermal ensemble MD
simulations predict a temperature-driven $\alpha \rightarrow \beta$ phase
transition at the single-layer limit, as revealed by both local atomic
displacement and Steinhardt's bond orientational order parameter $Q_4$. Our
work paves the way for further research on the dynamics of ferroelectric
$\alpha$-In$_2$Se$_3$ and related systems.
