Welcome to motif-learn, a Python package designed to apply machine learning techniques to scanning transmission electron microscopy (STEM) data. This tool enables researchers to identify and analyze structural motifs in atomic resolution images efficiently, offering a powerful way to explore materials with defects. 🚀
pip install git+https://github.com/jiadongdan/motif-learn.git- 📘Introduction to Zernike polynimials.
- 🔧How to use
ZPs? - 🔷How to extract symmetry information using
zmoments? - 🧩How to automatically estimate patch size from image?
motif-learn is licensed under the MIT License. For more details, see the LICENSE file.
If you find this project useful, please cite:
Dan, Jiadong, Xiaoxu Zhao, Shoucong Ning, Jiong Lu, Kian Ping Loh, Qian He, N. Duane Loh, and Stephen J. Pennycook. "Learning motifs and their hierarchies in atomic resolution microscopy." Science Advances 8, no. 15 (2022): eabk1005. 📄[paper]
Dan, Jiadong, Cheng Zhang, Xiaoxu Zhao and N. Duane Loh. " Symmetry quantification and segmentation in STEM imaging through Zernike moments." Chinese Physics B, (2024). 📄[paper]