In situ TEM is by far the most commonly used microscopy method for imaging dislocations, i.e., line-like defects in crystalline materials. However, quantitative image analysis so far was not possible, implying that also statistical analyses were strongly limited. In this work, we created a deep learning-based digital twin of an in situ TEM straining experiment, additionally allowing to perform matching simulations. As application we extract spatio-temporal information of moving dislocations from experiments carried out on a Cantor high entropy alloy and investigate the universality class of plastic strain avalanches. We can directly observe “stick–slip motion” of single dislocations and compute the corresponding avalanche statistics. The distributions turn out to be scale-free, and the exponent of the power law distribution exhibits independence on the driving stress. The introduced methodology is entirely generic and has the potential to turn meso-scale TEM microscopy into a truly quantitative and reproducible approach.

中文翻译：

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实现原位 TEM 实验的定量分析：一种针对位错动力学量身定制的高通量、基于深度学习的方法


原位 TEM 是迄今为止最常用的位错成像显微镜方法，即晶体材料中的线状缺陷。然而，到目前为止，定量图像分析是不可能的，这意味着统计分析也受到了很大的限制。在这项工作中，我们创建了一个基于深度学习的原位 TEM 应变实验数字孪生，此外还允许进行匹配模拟。作为应用，我们从对 Cantor 高熵合金进行的实验中提取移动位错的时空信息，并研究塑性应变雪崩的普遍性类别。我们可以直接观察单个位错的“粘滑运动”，并计算相应的雪崩统计数据。这些分布被证明是无标度的，并且幂律分布的指数在驱动应力上表现出独立性。引入的方法完全通用，有可能将介尺度 TEM 显微镜转变为真正的定量和可重复的方法。