利用4D电子衍射数据集训练人工神经网络进行精确定位和应变映射
Training artificial neural networks for precision orientation and strain mapping using 4D electron diffraction datasets
摘要：
描述了使用模拟的动态电子衍射图案来训练人工神经网络（ANN）和卷积神经网络（CNN）的技术。前提是基于以下事实。首先，在给定合适的晶体结构模型和散射势的情况下，利用动力学衍射理论可以精确地模拟电子衍射花样。其次，使用模拟的衍射图案作为输入，可以训练神经网络用于确定晶体结构特性，例如晶体取向和局部应变。此外，通过将训练的ANN应用于使用扫描电子纳米衍射（SEND）或4D扫描透射电子显微镜（4D-STEM）技术收集的四维衍射数据集（4D-DD），可以以高空间分辨率映射晶体结构性质。在这里，我们展示了人工神经网络在高精度分析晶体取向和应变方面的可能性，并通过与以前的方法进行比较，对人工神经网络和CNN的性能进行了基准测试。在0.009度（0.16 mrad）的取向映射，在0.04%或更低的应变映射的灵敏度，并在计算性能的改进的角分辨率的30倍的改善被证明。
英文摘要：
Techniques for training artificial neural networks (ANNs) and convolutional neural networks (CNNs) using simulated dynamical electron diffraction patterns are described. The premise is based on the following facts. First, given a suitable crystal structure model and scattering potential, electron diffraction patterns can be simulated accurately using dynamical diffraction theory. Secondly, using simulated diffraction patterns as input, ANNs can be trained for the determination of crystal structural properties, such as crystal orientation and local strain. Further, by applying the trained ANNs to four-dimensional diffraction datasets (4D-DD) collected using the scanning electron nanodiffraction (SEND) or 4D scanning transmission electron microscopy (4D-STEM) techniques, the crystal structural properties can be mapped at high spatial resolution. Here, we demonstrate the ANN-enabled possibilities for the analysis of crystal orientation and strain at high precision and benchmark the performance of ANNs and CNNs by comparing with previous methods. A factor of thirty improvement in angular resolution at 0.009 degrees (0.16 mrad) for orientation mapping, sensitivity at 0.04% or less for strain mapping, and improvements in computational performance are demonstrated.
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