Opto-Electro-Mechanical control of Ferroelectric Topological Structures for Ultralow Power Topotronic Devices using Hybrid Neural Network Quantum Molecular Dynamics and Molecular Mechanics Simulations

The recent discovery of the formation of complex polar topological structures such as skyrmions and merons in paraelectric(PE)/ferroelectric(FE) oxide heterostructures has opened the field of ferroelectric topotronics for development of ultralow power electronic devices.  To understand how to electrically and mechanically manipulate these complex structures we have developed a hybrid Neural-Network (NN) and Molecular Mechanics (MM) approach. Analogous to modern Quantum Mechanical (QM)/MM forcefield approaches, a highly scalable neural network is trained on ab-intio molecular dynamics (MD) simulations to encapsulate the complex polarization dynamics within the ferroelectric layer and a classical forcefield is tuned to correctly describe the strain and polarization fields applied by the paraelectric layer.  We examine the effects of strain, shear, electric field, and light induced electronic excitation to manipulate topological structures in PE/FE oxide heterostructures composed of SrTiO₃(STO) and PbTiO₃(PTO). The hybrid NN/MM approach developed here can perform up to billion atom MD simulations, offering an exciting new avenue for exploring opto-electro-mechanical control ultralow power topological based devices.