Identifying point defects and other structural anomalies using scanning transmission electron microscopy (STEM) is important to understand a material's properties caused by the disruption of the regular pattern of crystal lattice. Due to improvements in instrumentation stability and electron optics, atomic‐resolution images with a field of view of several hundred nanometers can now be routinely acquired at 1–10 Hz frame rates and such data, which often contain thousands of atomic columns, need to be analyzed. To date, image analysis is performed largely manually, but recent developments in computer vision (CV) and machine learning (ML) now enable automated analysis of atomic structures and associated defects. Here, the authors report on how a Convolutional Variational Autoencoder (CVAE) can be utilized to detect structural anomalies in atomic‐resolution STEM images. Specifically, the training set is limited to perfect crystal images , and the performance of a CVAE in differentiating between single‐crystal bulk data or point defects is demonstrated. It is found that the CVAE can reproduce the perfect crystal data but not the defect input data. The disagreesments between the CVAE‐predicted data for defects allows for a clear and automatic distinction and differentiation of several point defect types.

ATHENA has been designed as a general purpose detector capable of delivering the full scientific scope of the Electron-Ion Collider. Careful technology choices provide fine tracking and momentum resolution, high performance electromagnetic and hadronic calorimetry, hadron identification over a wide kinematic range, and near-complete hermeticity. This article describes the detector design and its expected performance in the most relevant physics channels. It includes an evaluation of detector technology choices, the technical challenges to realizing the detector and the R&D required to meet those challenges.

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