The emergence of commercial electron backscatter diffraction (EBSD) equipment ushered in an era of information rich maps produced by determining the orientation of user-selected crystal structures. Since then, a technological revolution has occurred in the quality, rate detection, and analysis of these diffractions patterns. The next revolution in EBSD is the ability to directly utilize the information rich diffraction patterns in a high-throughput manner. Aided by machine learning techniques, this new methodology is, as demonstrated herein, capable of accurately separating phases in a material by crystal symmetry, chemistry, and even lattice parameters with fewer human decisions. This work is the first demonstration of such capabilities and addresses many of the major challenges faced in modern EBSD. Diffraction patterns are collected from a variety of samples, and a convolutional neural network, a type of machine learning algorithm, is trained to autonomously recognize the subtle differences in the diffraction patterns and output phase maps of the material. This study offers a path to machine learning coupled phase mapping as databases of EBSD patterns encompass an increasing number of the possible space groups, chemistry changes, and lattice parameter variations.

中文翻译：

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使用卷积神经网络的 EBSD 中的相位映射

商用电子背散射衍射 (EBSD) 设备的出现开创了一个信息丰富的地图时代，该地图通过确定用户选择的晶体结构的方向来生成。从那时起，在这些衍射图案的质量、速率检测和分析方面发生了技术革命。EBSD 的下一次革命是以高通量方式直接利用信息丰富的衍射图案的能力。如本文所示，在机器学习技术的帮助下，这种新方法能够通过晶体对称性、化学甚至晶格参数准确地分离材料中的相，而无需人工决策。这项工作是此类能力的首次展示，并解决了现代 EBSD 面临的许多主要挑战。从各种样本中收集衍射图案，并训练卷积神经网络（一种机器学习算法）以自主识别材料的衍射图案和输出相位图的细微差异。这项研究为机器学习耦合相位映射提供了一条途径，因为 EBSD 模式的数据库包含越来越多的可能空间群、化学变化和晶格参数变化。