Download
AtomAI is a Pytorch-based package for deep and machine-learning analysis of microscopy data that doesn't require any advanced knowledge of Python or machine learning. The intended audience is domain scientists with a basic understanding of how to use NumPy and Matplotlib. It was developed by Maxim Ziatdinov at Oak Ridge National Lab. The purpose of the AtomAI is to provide an environment that bridges the instrument-specific libraries and general physical analysis by enabling the seamless deployment of machine learning algorithms including deep convolutional neural networks, invariant variational autoencoders, and decomposition/unmixing techniques for image and hyperspectral data analysis. Ultimately, it aims to combine the power and flexibility of the PyTorch deep learning framework and the simplicity and intuitive nature of packages such as scikit-learn, with a focus on scientific data.
Features
- Exploring causal physical mechanisms via non-Gaussian linear models and deep kernel learning
- Exploring order parameters and dynamic processes in disordered systems via variational autoencoders
- Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning
- Semantic segmentation
- ImSpec models
- Documentation available
Project Samples
