Table of Contents
QEM provides a robust, fast, and user-friendly toolkit for quantitative analysis in advanced microscopy. Its primary goal is to build highly accurate atomic models by refining structural parameters directly from experimental data, effectively linking electron microscopy with computational materials science.
The project is a Python-based evolution of the well-regarded StatSTEM. You may also want to check Atomap serving similar functions. This repo aims to go beyond state-of-the-art and is currently under heavy development.
QEM currently support the following features
- Atomic Model Refinement: Precisely determine scattering cross-sections, atomic positions, and the number of atoms for each atomic column.
- High-Performance Computing: Achieves significant speed-ups through GPU acceleration, model-based parameter estimation is performed via gradient descent.
- Flexibility of model choices (Gaussian, Lorentzian, Voigt, ect)
- Multi-elemental column refinement with same/different width for each element/column
- Flexible Backend: Built on Keras, it supports multiple backends like JAX, TensorFlow, and PyTorch, giving users the freedom to choose their preferred framework.
- Bridging Fields: Creates a seamless workflow between experimental microscopy and computational materials modeling via ASE.
- Atomic column assignment with input crystal structure and spatial mask
- Displacement and strain mapping
You can check the example tutorials in the examples
To get code running locally, let's first create a conda environment.
conda create -n qem python=3.11
conda activate qem
pip install -e .
cd examples- Model based on Keras to support different backend (Tensorflow, PyTorch, JAX)
- Test the code on Windows
- Improve Gaussian mixture model and selection rule
- Atom counting based on simulated library
- Better peak initialisation
- Perovskite polarisation, oxygen octahedron tilt and distortion
- Add documentation
- Add tests
- User-friendly GUI
See the open issues for a full list of proposed features (and known issues).
Contributions are what makes the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the GPLv3 License. See LICENSE for more information.
Zezhong Zhang - [email protected]
Project Link: https://github.com/zezhong-zhang/qem
- Readme page based on Best-README-Template