  Complex strain status can exist in 2D materials during their synthesis
process, resulting in significant impacts on the physical and chemical
properties. Despite their prevalence in experiments, their influence on the
material properties and the corresponding mechanism are often understudied due
to the lack of effective simulation methods. In this work, we investigated the
effects of bending, rippling, and bubbling on the ferroelectric domains in
In2Se3 monolayer by density functional theory (DFT) and deep learning molecular
dynamics (DLMD) simulations. The analysis of the tube model shows that bending
deformation imparts asymmetry into the system, and the polarization direction
tends to orient towards the tensile side, which has a lower energy state than
the opposite polarization direction. The energy barrier for polarization
switching can be reduced by compressive strain according DFT results. The
dynamics of the polarization switching is investigated by the DLMD simulations.
The influence of curvature and temperature on the switching time follows the
Arrhenius-style function. For the complex strain status in the rippling and
bubbling model, the lifetime of the local transient polarization is analyzed by
the autocorrelation function, and the size of the stable polarization domain is
identified. Local curvature and temperature can influence the local
polarization dynamics following the proposed Arrhenius-style equation. Through
cross-scale simulations, this study demonstrates the capability of
deep-learning potentials in simulating polarization for ferroelectric
materials. It further reveals the potential to manipulate local polarization in
ferroelectric materials through strain engineering.
