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README.md

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+---
+license: mit
+tags:
+- crystal-generation
+- diffusion-transformer
+- materials-science
+- pytorch
+language:
+- en
+library_name: pytorch
+pipeline_tag: other
+---
+
+# CrystalDiT: A Diffusion Transformer for Crystal Generation
+
+[![GitHub](https://img.shields.io/badge/GitHub-CrystalDiT-blue)](https://github.com/xiaohan-yi/CrystalDiT)
+[![Paper](https://img.shields.io/badge/arXiv-2508.16614-red)](https://arxiv.org/abs/2508.16614)
+
+**CrystalDiT** is a simplified diffusion transformer architecture for crystal structure generation that achieves state-of-the-art performance by treating lattice and atomic properties as a single, interdependent system.
+
+CrystalDiT achieves **9.62% SUN rate** on MP-20, significantly outperforming existing methods like FlowMM (4.38%) and MatterGen (3.42%).
+
+Key features:
+* **Unified Architecture**: Joint attention processing of lattice and atomic features
+* **Chemical Representation**: Two-dimensional atomic encoding using periodic table positions  
+* **Balance Score**: Novel model selection metric optimizing discovery potential vs. generation quality
+
+This checkpoint represents the best-performing model selected via Balance Score methodology after training for 50,000 epochs on the MP-20 dataset.
+
+## Files in this Repository
+
+- **`best_model.pt`**: Pre-trained CrystalDiT model checkpoint (best model selected via Balance Score)
+- **`generate_crystals.tar`**: Generated crystal structures from all compared methods, containing:
+  - **`CrystalDiT_crystals/`**: 10,000 structures from our method (9.62% SUN rate)
+  - **`flowmm_crystals/`**: 10,000 structures from FlowMM baseline (4.38% SUN rate)  
+  - **`mattergen_crystals/`**: 10,000 structures from MatterGen baseline (3.42% SUN rate)
+  - **`ADiT_crystals_mp20/`**: 10,000 structures from ADiT baseline (2.74% SUN rate)
+  - **`diffcep_crystals/`**: 10,000 structures from DiffCSP baseline
+  - **`diffcsp-pp_crystals/`**: 10,000 structures from DiffCSP++ baseline
+
+All crystal structures are in CIF format and were used for the comparative evaluation in our paper. These are provided to facilitate reproducible research and fair comparison with future methods.
+
+## Usage
+
+Extract the generated structures:
+```bash
+tar -xf generate_crystals.tar
+```
+
+Load the model checkpoint as described in the **README on GitHub**.
+
+## Performance
+
+| Method | SUN (%) | MSUN (%) | UN Rate (%) |
+|---------|---------|----------|-------------|
+| FlowMM | 4.38 | 20.16 | 87.66 |
+| MatterGen | 3.42 | 23.91 | 89.89 |
+| ADiT | 2.74 | 13.50 | 37.08 |
+| **CrystalDiT** | **9.62** | **25.94** | **63.28** |
+
+## Citation
+
+```bibtex
+@article{yi2024crystaldit,
+  title={CrystalDiT: A Diffusion Transformer for Crystal Generation},
+  author={Yi, Xiaohan and Xu, Guikun and Xiao, Xi and Zhang, Zhong and Liu, Liu and Bian, Yatao and Zhao, Peilin},
+  journal={arXiv preprint arXiv:2508.16614},
+  year={2024},
+  url={https://arxiv.org/abs/2508.16614}
+}
+```