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

arXiv: https://arxiv.org/abs/2105.07485 · HTML: https://mlsanity.com/paper/2105.07485/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 98 references that are in the map.
- First posted on arXiv 16 May 2021.

## Builds on (most cited first, 3 of 3)

- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Improving the Segmentation of Scanning Probe Microscope Images using Convolutional Neural Networks](https://mlsanity.com/paper/2008.12371/) (2020)
- [Scanning Probe State Recognition With Multi-Class Neural Network Ensembles](https://mlsanity.com/paper/1903.09101/) (2019)

## Built on by (most cited first, 19 of 19)

- [Towards Lightweight Data Integration using Multi-workflow Provenance and Data Observability](https://mlsanity.com/paper/2308.09004/) (2023)
- [An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics](https://mlsanity.com/paper/2109.14772/) (2021)
- [Optimizing Training Trajectories in Variational Autoencoders via Latent Bayesian Optimization Approach](https://mlsanity.com/paper/2207.00128/) (2022)
- [Physics and Chemistry from Parsimonious Representations: Image Analysis via Invariant Variational Autoencoders](https://mlsanity.com/paper/2303.18236/) (2023)
- [Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis](https://mlsanity.com/paper/2302.04216/) (2023)
- [SAM-I-Am: Semantic Boosting for Zero-shot Atomic-Scale Electron Micrograph Segmentation](https://mlsanity.com/paper/2404.06638/) (2024)
- [Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows](https://mlsanity.com/paper/2208.02865/) (2022)
- [Causal Discovery from Data Assisted by Large Language Models](https://mlsanity.com/paper/2503.13833/) (2025)
- [Affinity-VAE: incorporating prior knowledge in representation learning from scientific images](https://mlsanity.com/paper/2209.04517/) (2022)
- [Accelerating Domain-Aware Electron Microscopy Analysis Using Deep Learning Models with Synthetic Data and Image-Wide Confidence Scoring](https://mlsanity.com/paper/2408.01558/) (2024)
- [Revealing the Evolution of Order in Materials Microstructures Using Multi-Modal Computer Vision](https://mlsanity.com/paper/2411.09896/) (2024)
- [Defect Detection in Photolithographic Patterns Using Deep Learning Models Trained on Synthetic Data](https://mlsanity.com/paper/2505.10192/) (2025)
- [Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes](https://mlsanity.com/paper/2511.08350/) (2025)
- [From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry](https://mlsanity.com/paper/2605.03205/) (2026)
- [Performing Video Frame Prediction of Microbial Growth with a Recurrent Neural Network](https://mlsanity.com/paper/2205.05810/) (2022)
- [Detecting Atomic Scale Surface Defects in STM of TMDs with Ensemble Deep Learning](https://mlsanity.com/paper/2312.05160/) (2023)
- [Atom identification in bilayer moire materials with Gomb-Net](https://mlsanity.com/paper/2502.09791/) (2025)
- [AtomDiffuser: Time-Aware Degradation Modeling for Drift and Beam Damage in STEM Imaging](https://mlsanity.com/paper/2508.10359/) (2025)
- [Schema-Bound LLM Control of Scientific Instrumentation through Model Context Protocol Skills](https://mlsanity.com/paper/2607.17012/) (2026)

## Abstract (arXiv)

AtomAI is an open-source software package bridging instrument-specific Python libraries, deep learning, and simulation tools into a single ecosystem. AtomAI allows direct applications of the deep convolutional neural networks for atomic and mesoscopic image segmentation converting image and spectroscopy data into class-based local descriptors for downstream tasks such as statistical and graph analysis. For atomically-resolved imaging data, the output is types and positions of atomic species, with an option for subsequent refinement. AtomAI further allows the implementation of a broad range of image and spectrum analysis functions, including invariant variational autoencoders (VAEs). The latter consists of VAEs with rotational and (optionally) translational invariance for unsupervised and class-conditioned disentanglement of categorical and continuous data representations. In addition, AtomAI provides utilities for mapping structure-property relationships via im2spec and spec2im type of encoder-decoder models. Finally, AtomAI allows seamless connection to the first principles modeling with a Python interface, including molecular dynamics and density functional theory calculations on the inferred atomic position. While the majority of applications to date were based on atomically resolved electron microscopy, the flexibility of AtomAI allows straightforward extension towards the analysis of mesoscopic imaging data once the labels and feature identification workflows are established/available. The source code and example notebooks are available at https://github.com/pycroscopy/atomai.