Induction and Ferroelectric Switching of Flux Closure Domains in Strained PbTiO3 with Neural Network Quantum Molecular Dynamics

Wehave developed an extension of the Neural Network Quantum MolecularDynamics (NNQMD) simulation method to incorporate electric-field dynamicsbased on Born effective charge (BEC), called NNQMD-BEC. We first validateNNQMD-BEC for the switching mechanisms of archetypal ferroelectricPbTiO(3) bulk crystal and 180 & DEG; domain walls (DWs). NNQMD-BECsimulations correctly describe the nucleation-and-growth mechanismduring DW switching. In triaxially strained PbTiO3 withstrain conditions commonly seen in many superlattice configurations,we find that flux-closure texture can be induced with applicationof an electric field perpendicular to the original polarization direction.Upon field reversal, the flux-closure texture switches via a pairof transient vortices as the intermediate state, indicating an energy-efficientswitching pathway. Our NNQMD-BEC method provides a theoretical guidanceto study electro-mechano effects with existing machine learning forcefields using a simple BEC extension, which will be relevant for engineeringapplications such as field-controlled switching in mechanically strainedferroelectric devices.