What Can DP Do too? | Simulating Ferroelectric Topological Structures with Deep Learning Potentials
Recently, the research group led by Shi Liu from the Department of Physics, School of Science, Westlake University, based on their previous work "Modular development of deep potential" (ModDP) [1], utilized the deep potential of the solid solution Pbx Sr1 - x TiO3 to simulate the full - composition superlattices of the (PbTiO3)10/(Pbx Sr1 - x TiO3)10 system within the range of 0 ≤ x ≤ 1. This revealed a rich phase diagram derived from topological structures. In this study, the researchers took the typical system of (PbTiO3)10/(Pbx Sr1 - x TiO3)10 as a starting point to simulate the special phase transition of vortex domains in the superlattice during the heating process, namely, the ferroelectric - like - antiferroelectric - like - paraelectric phase transition. Moreover, the ferroelectric - like phase and the antiferroelectric - like phase can be respectively regulated under an external electric field, thus achieving two different types of electric hysteresis loops. By introducing Pb doping into the layer, the researchers also found that due to the weakening of the depolarization field, a topological phase transition from the vortex state to the skyrmion state can be induced. The relevant research results, titled "Topological phase transitions in perovskite superlattices driven by temperature, electric field, and doping", were published in Physical Review B [2]. Doctoral student Jiyuan Yang is the first author.