Python Toolkit for X-ray Diffraction Simulation, Analysis, and AI-driven Structure Refinement

Project Homepage · Algorithm Documentation · Paper (arXiv) · Supplementary Information · Download Statistics

Other tools include XQueryer for initial structure inference and PRDNet for crystal property prediction.

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Overview

PyXplore is a modular Python framework for X-ray diffraction (XRD) simulation, decomposition, quantitative analysis, and AI-assisted structure refinement.

It integrates:

• Physics-based diffraction modeling
• EM-based Bragg optimization
• Structure graph construction
• Extinction and Wyckoff analysis
• Amorphous phase quantification
• AI-driven structural refinement

The toolkit is designed for reproducible scientific workflows in materials characterization and AI for Science research.

Install from PyPI and Install the dependencies:

pip install PyXplore

Upgrade to the latest version:

pip install --upgrade PyXplore

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Key Features

• XRD Simulation Accurate diffraction pattern generation from crystallographic information.

• Peak Decomposition & Quantitative Analysis WPEM-based decomposition and volume fraction determination.

• Bragg Law Optimization (EM Framework) Expectation-Maximization-based parameter solving.

• Extinction & Wyckoff Handling Symmetry-aware preprocessing and structural filtering.

• Graph-Based Structure Representation Crystal graph construction for downstream machine learning tasks.

• Amorphous Structure Analysis RDF-based quantitative evaluation.

• Multi-modal Extension Integrated modules for XAS and XPS analysis.

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Architecture Overview

PyWPEM/
├── WPEM.py
├── XRDSimulation/
├── EMBraggOpt/
├── Refinement/
├── StructureOpt/
├── GraphStructure/
├── Extinction/
├── Amorphous/
├── Background/
├── Plot/
├── DecomposePlot/
├── WPEMXAS/
├── WPEMXPS/
└── refs/

The design follows a physics-consistent, modular architecture, enabling independent or pipeline-based execution.

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Tables & Figures

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Scientific Reference

If you use PyWPEM in your research, please cite:

@article{cao2026wpem,
  title={AI-Driven Structure Refinement of X-ray Diffraction},
  author={Bin Cao, Qian Zhang, Zhenjie Feng, Taolue Zhang, Jiaqiang Huang, Lu-Tao Weng, Tong-Yi Zhang},
  journal={arXiv preprint},
  year={2026},
  url={https://arxiv.org/abs/2602.16372v1}
}

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Contributing

We welcome contributions from the community.

• Report bugs via Issues
• Propose features
• Submit pull requests
• Contact for academic collaboration

Please ensure code readability, documentation clarity, and scientific correctness before submission.

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License

This project is released under the MIT License.

Free for academic and commercial use. Please cite related publications when used in scientific research.

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For any inquiries or assistance, feel free to contact Mr. CAO Bin at: 📧 Email: bcao686@connect.hkust-gz.edu.cn Cao Bin is a PhD candidate at the Hong Kong University of Science and Technology (Guangzhou), under the supervision of Professor Zhang Tong-Yi. His research focuses on AI for science, especially intelligent crystal-structure analysis and discovery. Learn more about his work on his homepage.