论文标题
通过无监督的机器学习，在动态压电力显微镜中解开铁电域壁几何和途径
Disentangling ferroelectric domain wall geometries and pathways in dynamic piezoresponse force microscopy via unsupervised machine learning
论文作者
论文摘要
通过动态压电力显微镜（PFM）可视化的铁电材料中的域开关途径，通过变异自动编码器（VAE）探索，这简化了观察到的域结构的元素，至关重要的是允许旋转不变性，从而减少了局部极性分布分布的可变性，从而减少了小量子数量的差异。对于小采样窗口尺寸，潜在空间是退化的，并且仅在单个潜在变量的方向上观察到可变性，该变量可以通过域壁的存在来识别。对于较大的窗口尺寸，潜在空间为2D，并且分离的潜在变量通常可以解释为域结构的开关和复杂性的程度。在监视域交换时，应用于多个连续的PFM图像，因此可以在潜在空间中可视化两极分化开关机制，从而深入了解域的演化机制及其与微结构的相关性。
Domain switching pathways in ferroelectric materials visualized by dynamic Piezoresponse Force Microscopy (PFM) are explored via variational autoencoder (VAE), which simplifies the elements of the observed domain structure, crucially allowing for rotational invariance, thereby reducing the variability of local polarization distributions to a small number of latent variables. For small sampling window sizes the latent space is degenerate, and variability is observed only in the direction of a single latent variable that can be identified with the presence of domain wall. For larger window sizes, the latent space is 2D, and the disentangled latent variables can be generally interpreted as the degree of switching and complexity of domain structure. Applied to multiple consecutive PFM images acquired while monitoring domain switching, the polarization switching mechanism can thus be visualized in the latent space, providing insight into domain evolution mechanisms and their correlation with the microstructure.
