Selective area electron diffraction (SAED) patterns can provide valuable insight into the structure of a material. However, the manual identification of collected patterns can be a significant bottleneck in the overall phase classification workflow. In this work, we utilize the recent advances in computer vision and machine learning (ML) to automate the indexing of SAED patterns. The performance of six different ML algorithms is demonstrated using metallic plutonium-zirconium alloys. The most successful approach trained a neural network (NN) to make a classification of the phase and zone axis, and then utilized a second NN to synthesize multiple independent predictions of different tilts in a single sample to make an overall phase identification. The results demonstrate that automated SAED phase identification using ML is a viable route to accelerate materials characterization.

中文翻译：

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使用机器学习自动进行选区电子衍射相识别


选区电子衍射 (SAED) 图案可以提供对材料结构的宝贵见解。然而，手动识别收集的模式可能是整个阶段分类工作流程中的一个重大瓶颈。在这项工作中，我们利用计算机视觉和机器学习 (ML) 的最新进展来自动化 SAED 模式的索引。使用金属钚锆合金演示了六种不同机器学习算法的性能。最成功的方法是训练神经网络 (NN) 对相位和区域轴进行分类，然后利用第二个神经网络合成单个样本中不同倾斜的多个独立预测，以进行整体相位识别。结果表明，使用 ML 进行自动 SAED 相识别是加速材料表征的可行途径。