在介观和原子水平上发现电子能量损失谱图像中的不变性空间特征
Discovering Invariant Spatial Features in Electron Energy Loss Spectroscopy Images on the Mesoscopic and Atomic Levels
摘要：
在过去的二十年中，使用扫描透射电子显微镜（STEM）的电子能量损失谱（EELS）成像已经成为用于可视化复杂材料和结构中的复杂化学、电子、等离子体和声子现象的首选技术。EELS数据的可用性需要开发方法来分析具有复杂空间和能量结构的多维数据集。传统上，这些数据集的分析是基于对单个光谱的分析，一次一个，而包含托换过程物理学相关信息的单个空间像素之间的空间结构和相关性通常被忽略，仅通过可视化作为2D地图进行分析。在这里，我们开发了一种基于机器学习的方法和工作流程，用于使用降维和多通道旋转不变变分自动编码器的组合来分析3D EELS数据集中的空间结构。这种方法被示出为两个系统中的纳米线和在功能氧化物中的核心激发使用低损耗和核心损耗EELS，分别等离子现象的分析。本手稿中开发的代码是开源的，可以免费获得，并作为一个笔记本提供给感兴趣的读者。
英文摘要：
Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope (STEM) has emerged as a technique of choice for visualizing complex chemical, electronic, plasmonic, and phononic phenomena in complex materials and structures. The availability of the EELS data necessitates the development of methods to analyze multidimensional datasets with complex spatial and energy structures. Traditionally, the analysis of these data sets has been based on analysis of individual spectra, one at a time, whereas the spatial structure and correlations between individual spatial pixels containing the relevant information of the physics of underpinning processes have generally been ignored and analyzed only via the visualization as 2D maps. Here we develop a machine learning-based approach and workflows for the analysis of spatial structures in 3D EELS data sets using a combination of dimensionality reduction and multichannel rotationally-invariant variational autoencoders. This approach is illustrated for the analysis of both the plasmonic phenomena in a system of nanowires and in the core excitations in functional oxides using low loss and core loss EELS, respectively. The code developed in this manuscript is open sourced and freely available and provided as a Jupyter notebook for the interested reader here.
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