  The information content of atomic resolution scanning transmission electron
microscopy (STEM) images can often be reduced to a handful of parameters
describing each atomic column, chief amongst which is the column position.
Neural networks (NNs) are a high performance, computationally efficient method
to automatically locate atomic columns in images, which has led to a profusion
of NN models and associated training datasets. We have developed a benchmark
dataset of simulated and experimental STEM images and used it to evaluate the
performance of two sets of recent NN models for atom location in STEM images.
Both models exhibit high performance for images of varying quality from several
different crystal lattices. However, there are important differences in
performance as a function of image quality, and both models perform poorly for
images outside the training data, such as interfaces with large difference in
background intensity. Both the benchmark dataset and the models are available
using the Foundry service for dissemination, discovery, and reuse of machine
learning models.
