Understanding transformations under electron beam irradiation requires mapping the structural phases and their evolution in real time. To date, this has mostly been a manual endeavor comprising difficult frame-by-frame analysis that is simultaneously tedious and prone to error. Here, we turn toward the use of deep convolutional neural networks (DCNN) to automatically determine the Bravais lattice symmetry present in atomically resolved images. A DCNN is trained to identify the Bravais lattice class given a 2D fast Fourier transform of the input image. Monte-Carlo dropout is used for determining the prediction probability, and results are shown for both simulated and real atomically resolved images from scanning tunneling microscopy and scanning transmission electron microscopy. A reduced representation of the final layer output allows to visualize the separation of classes in the DCNN and agrees with physical intuition. We then apply the trained network to electron beam-induced transformations in WS₂, which allows tracking and determination of growth rate of voids. We highlight two key aspects of these results: (1) it shows that DCNNs can be trained to recognize diffraction patterns, which is markedly different from the typical “real image” cases and (2) it provides a method with in-built uncertainty quantification, allowing the real-time analysis of phases present in atomically resolved images.

了解电子束辐照下的转变需要实时绘制结构相及其演变。迄今为止，这主要是手动的工作，包括同时进行乏味且容易出错的困难的逐帧分析。在这里，我们转向使用深度卷积神经网络（DCNN）自动确定原子分解图像中存在的Bravais晶格对称性。给定输入图像的2D快速傅立叶变换，对DCNN进行训练以识别Bravais晶格类别。蒙特卡洛辍学率用于确定预测概率，并显示了来自扫描隧道显微镜和扫描透射电子显微镜的模拟和真实原子分辨图像的结果。最终层输出的简化表示可以可视化DCNN中的类分离，并与物理直觉相符。然后，我们将训练后的网络应用于WS中电子束诱导的转换_参照图2，其允许跟踪和确定空隙的生长速率。我们重点介绍了这些结果的两个关键方面：（1）它表明可以训练DCNN识别衍射图样，这与典型的“真实图像”情况明显不同；（2）它提供了一种内置不确定性量化的方法，可以对原子分辨图像中存在的相位进行实时分析。