Metadata-Version: 2.4
Name: diffcrysgen
Version: 0.1.0
Summary: "DiffCrysGen is a score-based diffusion model for accelerated design of diverse inorganic crystalline materials."
Home-page: https://github.com/SouravMal/DiffCrysGen.git
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch~=2.5.1
Requires-Dist: numpy~=2.0.1
Requires-Dist: pandas~=2.2.3
Requires-Dist: scikit-learn~=1.6.1
Requires-Dist: tqdm
Requires-Dist: ase~=3.24.0
Requires-Dist: spglib~=2.5.0
Requires-Dist: joblib
Dynamic: license-file

# DiffCrysGen [![Project Version](https://img.shields.io/badge/version-v0.1.0-blue)](https://github.com/SouravMal/DiffCrysGen)

DiffCrysGen is a score-based diffusion model. It treats the entire materials representation with a single, unified diffusion process, allowing a single denosing neural network to predict a holistic score for the entire noisy crystal data. This unified treatment significantly simplifies the architecture and improves the computational efficiency.


<p align="center">
  <img src="images/logo-DiffCrysGen.png" alt="DiffCrysGen Logo" width="350"/>
</p>


## Generative diffusion framework in DiffCrysGen
<img src="images/diffusion-schematic.png" alt="DiffCrysGen Schematic" width="550">

---

## Architecture of the denoising neural network
<img src="images/architecture.png" alt="DiffCrysGen Architecture" width="750">

---

## Installation

### Prerequisites

The package requires specific environments and dependencies.
Using a virtual environment is highly recommended.
**Environment Setup using Conda**

```
conda create -n diffcrysgen python=3.11
conda activate diffcrysgen
```

### Install from PyPI
```
pip install diffcrysgen
```

### Install from Source Code
```
git clone https://github.com/SouravMal/DiffCrysGen.git
cd DiffCrysGen
pip install -e .
```

## Quick Start
For a simple walkthrough of generating materials and analyzing them, see the [DiffCrysGen Demo Notebook](./notebooks/DiffCrysGen-demo.ipynb).

## License

This project is licensed under the **MIT License**.

See the [LICENSE](LICENSE) file for details.

Developed by: [Sourav Mal](https://github.com/SouravMal) at Harish-Chandra Research Institute (HRI) (https://www.hri.res.in/), Prayagraj, India.


## Citation

Please consider citing our work if you find it helpful:

```bibtex
@misc{mal2025generativediffusionmodeldiffcrysgen,
      title={Generative Diffusion Model DiffCrysGen Discovers Rare Earth-Free Magnetic Materials}, 
      author={Sourav Mal and Nehad Ahmed and Subhankar Mishra and Prasenjit Sen},
      year={2025},
      eprint={2510.12329},
      archivePrefix={arXiv},
      primaryClass={cond-mat.mtrl-sci},
      url={https://arxiv.org/abs/2510.12329}, 
}
```


## Contact

If you have any questions, feel free to reach us at:
**Sourav Mal** <souravmal492@gmail.com> 
