Electron backscatter diffraction (EBSD) is one of the primary tools in materials development and analysis. The technique can perform simultaneous analyses at multiple length scales, providing local sub-micron information mapped globally to centimeter scale. Recently, a series of technological revolutions simultaneously increased diffraction pattern quality and collection rate. After collection, current EBSD pattern indexing techniques (whether Hough-based or dictionary pattern matching based) are capable of reliably differentiating between a “user selected” set of phases, if those phases contain sufficiently different crystal structures. EBSD is currently less well suited for the problem of phase identification where the phases in the sample are unknown. A pattern analysis technique capable of phase identification, utilizing the information-rich diffraction patterns potentially coupled with other data, such as EDS-derived chemistry, would enable EBSD to become a high-throughput technique replacing many slower (X-ray diffraction) or more expensive (neutron diffraction) methods. We utilize a machine learning technique to develop a general methodology for the space group classification of diffraction patterns; this is demonstrated within the $\lpar 4/m\comma \;\bar{3}\comma \;\;2/m\rpar$ point group. We evaluate the machine learning algorithm's performance in real-world situations using materials outside the training set, simultaneously elucidating the role of atomic scattering factors, orientation, and pattern quality on classification accuracy.

中文翻译：

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EBSD 中支持深度神经网络的空间组识别

电子背散射衍射 (EBSD) 是材料开发和分析的主要工具之一。该技术可以在多个长度尺度上同时进行分析，提供全局映射到厘米尺度的局部亚微米信息。最近，一系列技术革命同时提高了衍射图案的质量和采集率。收集后，当前的 EBSD 模式索引技术（无论是基于 Hough 还是基于字典模式匹配）能够可靠地区分“用户选择”的一组相，如果这些相包含足够不同的晶体结构。EBSD 目前不太适用于样品中的相未知的相识别问题。一种能够进行相位识别的模式分析技术，利用信息丰富的衍射图案可能与其他数据（例如 EDS 衍生化学）相结合，将使 EBSD 成为一种高通量技术，取代许多较慢（X 射线衍射）或更昂贵（中子衍射）的方法。我们利用机器学习技术开发了衍射图案空间群分类的通用方法；这在$\lpar 4/m\逗号\;\bar{3}\逗号\;\;2/m\rpar$点群。我们使用训练集之外的材料评估机器学习算法在实际情况下的性能，同时阐明原子散射因子、方向和图案质量对分类精度的作用。