利用隐变量解析铁电体中畴结构与功能的关系
Toward Decoding the Relationship between Domain Structure and Functionality in Ferroelectrics via Hidden Latent Variables
摘要：
铁电材料中的极化转换机制从根本上与局部畴结构和结构缺陷的存在有关，这两者都可以充当成核和钉扎中心，并产生影响壁动力学的局部静电和机械去极化场。然而，一般的畴结构和极化动力学之间的相关机制只有微弱的探索，排除洞察相关的物理机制。在这里，使用卷积编码器-解码器网络探索铁电材料中的局部畴结构和切换行为之间的相关性，从而通过编码潜变量实现图像到光谱（im 2spec）和光谱到图像（spec 2 im）的转换。后者反映了这样的假设，即域结构和极化切换之间的关系是吝啬的，即是基于一个小的本地机制。潜变量分布和它们的真实空间表示的分析提供了对局部切换行为的可预测性的洞察，从而提供了相关的物理机制。我们进一步提出，这些相关关系被违反的区域，即从域结构的极化动力学的可预测性降低，代表了详细研究的明显目标，例如，在自动化实验的背景下。这种方法提供了一个工作流程，以建立局部光谱响应和局部结构之间的相关性的存在，并可以普遍应用于光谱成像技术，如PFM，扫描隧道显微镜（STM）和光谱，和电子能量损失谱（EELS）在扫描透射电子显微镜（STEM）。
英文摘要：
Polarization switching mechanisms in ferroelectric materials are fundamentally linked to local domain structure and presence of the structural defects, which both can act as nucleation and pinning centers and create local electrostatic and mechanical depolarization fields affecting wall dynamics. However, the general correlative mechanisms between domain structure and polarization dynamics are only weakly explored, precluding insight into the associated physical mechanisms. Here, the correlation between local domain structures and switching behavior in ferroelectric materials is explored using the convolutional encoder-decoder networks, enabling the image to spectral (im2spec) and spectral to image (spec2im) translations via encoding latent variables. The latter reflects the assumption that the relationship between domain structure and polarization switching is parsimonious, i.e. is based upon a small number of local mechanisms. The analysis of latent variables distributions and their real space representations provides insight into the predictability of the local switching behavior, and hence associated physical mechanisms. We further pose that the regions where these correlative relationships are violated, i.e. predictability of the polarization dynamics from domain structure is reduced, represent the obvious target for detailed studies, e.g. in the context of automated experiments. This approach provides a workflow to establish the presence of correlation between local spectral responses and local structure and can be universally applied to spectral imaging techniques such as PFM, scanning tunneling microscopy (STM) and spectroscopy, and electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM).
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