The deep neural networks were implemented using the Keras deep learning library (https://keras.io/). The neural networks had an encoder-decoder structure [SegNet-like architecture (50)] that allowed a pixel-level classification of input images. For the atom finder network, the encoder part consisted of six convolutional layers, all activated by a rectified linear unit function. The convolutional filters (kernels) in all the layers were of the size 3 by 3 and stride 1. The first convolutional layer had 64 filters, the second and third layers had 128 filters each, and the fourth to sixth layers had 256 filters each. The max pooling layers were placed after the first, third and sixth layers. The decoder part contained the same blocks of convolutional layers in reversed order with a bilinear interpolated upsampling instead of the max pooling units. The Adam optimizer (51) was used with categorical cross-entropy as the loss function. For the “defect sniffer” network, the encoder part consisted of three convolutional layers with 64, 128, and 256 filters of the size 3 by 3 and stride 1, activated by a rectified linear unit function. The max pooling layers were placed in between the layers. The decoder part contained the same blocks of convolutional layers in reversed order with the nearest-neighbor upsampling between them. The focal loss (52) was used for improving identification of the defect structures. To optimize this loss, the Adam optimizer was used. The accuracy scores on a test set for atom finder and defect sniffer were ≈96 and ≈99%, respectively. The graph structures were constructed using NetworkX library (https://networkx.github.io/). The latest version of a workflow for creating and exploring the defect libraries is available at https://github.com/pycroscopy/AICrystallographer/tree/master/LibraryNet.

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