The recently discovered ferroelectricity of van der Waals bilayers offers an unconventional route to improve the performance of devices. Key parameters such as switching field and speed depend on the static and dynamic properties of domain walls (DWs). Here we theoretically explore the properties of textures in stacking-engineered ferroelectrics from first principles. Employing a machine-learning potential model, we present results of large-scale atomistic simulations of stacking DWs and Moir\'e structure of boron nitride bilayers. We predict that the competition between the switching barrier of stable ferroelectric states and the in-plane lattice distortion leads to a DW width of the order of ten nanometers. DWs motion reduces the critical ferroelectric switching field of a monodomain by two orders of magnitude, while high domain-wall velocities allow domain switching on a picosecond-timescale. The superior performance compared to conventional ferroelectrics (or ferromagnets) may enable ultrafast and power-saving non-volatile memories. By twisting the bilayer into a stacking Moir\'e structure, the ferroelectric transforms into a super-paraelectric since DWs move under ultralow electric fields.

中文翻译：

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堆叠工程铁电体中铁电有序的超快切换动力学

最近发现的范德瓦尔斯双层铁电性提供了一条提高器件性能的非常规途径。开关场和速度等关键参数取决于畴壁 (DW) 的静态和动态特性。在这里，我们从第一原理理论上探索了堆叠工程铁电体中纹理的特性。我们采用机器学习潜力模型，展示了堆叠 DW 和氮化硼双层的莫尔结构的大规模原子模拟结果。我们预测，稳定铁电态的开关势垒与面内晶格畸变之间的竞争会导致 DW 宽度达到 10 纳米量级。DWs 运动将单畴的临界铁电开关场降低了两个数量级，而高畴壁速度允许在皮秒时间尺度上进行域切换。与传统铁电体（或铁磁体）相比的卓越性能可以实现超快和节能的非易失性存储器。通过将双层扭曲成堆叠的莫尔结构，由于 DW 在超低电场下移动，铁电体转变为超顺电体。