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AtomAI is an open-source software package bridging instrument-specific Pythonlibraries, deep learning, and simulation tools into a single ecosystem. AtomAIallows direct applications of the deep convolutional neural networks for atomicand mesoscopic image segmentation converting image and spectroscopy data intoclass-based local descriptors for downstream tasks such as statistical andgraph analysis. For atomically-resolved imaging data, the output is types andpositions of atomic species, with an option for subsequent refinement. AtomAIfurther allows the implementation of a broad range of image and spectrumanalysis functions, including invariant variational autoencoders (VAEs). Thelatter consists of VAEs with rotational and (optionally) translationalinvariance for unsupervised and class-conditioned disentanglement ofcategorical and continuous data representations. In addition, AtomAI providesutilities for mapping structure-property relationships via im2spec and spec2imtype of encoder-decoder models. Finally, AtomAI allows seamless connection tothe first principles modeling with a Python interface, including moleculardynamics and density functional theory calculations on the inferred atomicposition. While the majority of applications to date were based on atomicallyresolved electron microscopy, the flexibility of AtomAI allows straightforwardextension towards the analysis of mesoscopic imaging data once the labels andfeature identification workflows are established/available. The source code andexample notebooks are available at https://github.com/pycroscopy/atomai.

AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond