论文标题
球形 - 角暗场成像和敏感的微观结构相聚类，无监督的机器学习
Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine learning
论文作者
论文摘要
电子反向散射衍射是一种广泛使用的技术，用于晶体结构和方向的纳米尺度分析。合金固体溶液基质产生的反向散射图案及其有序的超晶格仅显示出极为细微的差异，这是由于不弹性散射的，该散射在相干衍射之前。我们表明，无监督的机器学习（使用PCA，NMF和自动编码器神经网络）非常适合精细的功能提取和超晶格/矩阵分类。将群集平均模式重新映射到衍射球，使我们可以将Kikuchi带轮廓与动态模拟进行比较，确认超晶格化学计量，并促进具有球形实体角孔径的虚拟成像。现在，该管道可以从扫描电子显微镜中的各种材料中对精美的晶体学细节进行无与伦比的映射。
Electron backscatter diffraction is a widely used technique for nano- to micro-scale analysis of crystal structure and orientation. Backscatter patterns produced by an alloy solid solution matrix and its ordered superlattice exhibit only extremely subtle differences, due to the inelastic scattering that precedes coherent diffraction. We show that unsupervised machine learning (with PCA, NMF, and an autoencoder neural network) is well suited to fine feature extraction and superlattice/matrix classification. Remapping cluster average patterns onto the diffraction sphere lets us compare Kikuchi band profiles to dynamical simulations, confirm the superlattice stoichiometry, and facilitate virtual imaging with a spherical solid angle aperture. This pipeline now enables unparalleled mapping of exquisite crystallographic detail from a wide range of materials within the scanning electron microscope.
