yaotianvector/STEM2Mat

AutoMat Benchmark: STEM Image to Crystal Structure

The AutoMat Benchmark is a multimodal dataset designed to evaluate deep‑learning systems for iDPC-STEM‑based crystal‑structure reconstruction and property prediction.

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📁 Dataset Structure

The dataset is organized into three tiers of increasing difficulty:

benchmark/
├── tier1/
│   ├── img/          # STEM images (e.g., PNG, TIFF)
│   ├── label/        # Atomic position labels (e.g., TXT, JSON)
│   └── cif_file/     # Reconstructed or ground‑truth CIF files
├── tier2/
│   └── ...           # Same sub‑folders as tier1
├── tier3/
│   └── ...
└── property.csv      # Material properties for all samples

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🔬 Tier Descriptions

Tier	Characteristics
Tier 1	Simulated low-noise STEM images, light elements, low complexity
Tier 2	Moderate noise or multiple elements, more realistic patterns
Tier 3	Low dose, multi-elements, complex symmetry

Each sample in the dataset includes:

• A STEM image (img/)
• Labeled atomic coordinates (label/)
• A reconstructed or reference CIF file (cif_file/)
• Associated material properties in property.csv

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📊 Tasks Supported

• STEM-to-structure inference
• CIF generation and comparison
• Atomic position prediction
• Property prediction (formation energy, energy_per_atom, bandgap, etc.)

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🔗 Files Description

File / Folder	Description
img/	STEM input images (grayscale microscopy)
label/	Atomic position data (format: .png per sample)
cif_file/	.cif crystal structure files for each sample
property.csv	Global material properties table with material_id match

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📄 License

This dataset is released under the MIT License. You are free to use, modify, and distribute with attribution.

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✉️ Citation

If you use this benchmark, please cite:

@misc{yang2025automatenablingautomatedcrystal,
      title={AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use}, 
      author={Yaotian Yang and Yiwen Tang and Yizhe Chen and Xiao Chen and Jiangjie Qiu and Hao Xiong and Haoyu Yin and Zhiyao Luo and Yifei Zhang and Sijia Tao and Wentao Li and Qinghua Zhang and Yuqiang Li and Wanli Ouyang and Bin Zhao and Xiaonan Wang and Fei Wei},
      year={2025},
      eprint={2505.12650},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.12650}, 
}

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🙋 Contact

For questions or collaborations, please contact: [email protected]