Spaces:
Running
Running
File size: 3,693 Bytes
fe7e2d0 d94c1ca fe7e2d0 d94c1ca fe7e2d0 783401a fe7e2d0 783401a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
---
title: MatterGPT CPU
emoji: 🖼
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 4.41.0
app_file: app.py
pinned: false
license: mit
thumbnail: >-
https://cdn-uploads.huggingface.co/production/uploads/66ae0d46f0bb529189cd3ecf/qYG09uLGuCtKbyhb9U99j.png
short_description: 'MatterGPT Demo is an innovative crystal inverse design tool '
---
# MatterGPT Demo: Crystal Inverse Designer
## Project Overview
MatterGPT Demo is an innovative crystal inverse design tool that leverages advanced machine learning techniques to generate crystal structures based on specified material properties. This project showcases the powerful capabilities of the MatterGPT model, which can inversely design material structures from desired band gaps and formation energies.
# [[Paper]](https://arxiv.org/abs/2408.07608) [[视频介绍]](https://www.bilibili.com/video/BV1HdeHeyEfP/) [github](https://github.com/xiaohang007/SLICES/tree/main/MatterGPT)
## Features
- **Inverse Design from Properties to Structures**: Input target band gap and formation energy to generate crystal structures that match these properties.
- **SLICES Encoding**: Utilizes SLICES (Simplified Line-Input Crystal-Encoding System) encoding to represent crystal structures.
- **Visualization**: Generated structures can be intuitively presented through 3D visualization.
- **Structure Analysis**: Provides detailed information about the generated structures, including chemical formula, lattice parameters, etc.
- **CIF File Export**: Download Crystallographic Information Files (CIF) of generated structures for further analysis and use.
## Installation
This project runs on Hugging Face Spaces, requiring no local installation. However, if you wish to run it locally, follow these steps:
1. Clone the repository:
```
git clone https://huggingface.co/spaces/your-username/mattergpt-demo
```
2. Install dependencies:
```
pip install -r requirements.txt
```
3. Run the application:
```
python app.py
```
## Usage
1. Access the MatterGPT Demo on Hugging Face Space.
2. Enter the target band gap (eV) and formation energy (eV/atom) in the interface.
3. Click the "Generate" button.
4. The system will generate SLICES encoding, 3D structure visualization, structure summary, and CIF file.
5. If you encounter any issues, use the "Reinitialize Resources" button to refresh the model and other components.
## Technical Details
- **Model**: Utilizes a pre-trained MatterGPT model
- **Backend**: Uses SLICES for structure decoding and optimization
- **Frameworks**: PyTorch, Gradio
- **Visualization**: Employs ASE (Atomic Simulation Environment) for structure rendering
## Notes
- The generation process may take some time. Please be patient.
- Due to the model's stochastic nature, the same input may produce different output structures.
- Generated structures are theoretical predictions and may require further experimental validation.
## Citation
```
@misc{chen2024mattergptgenerativetransformermultiproperty,
title={MatterGPT: A Generative Transformer for Multi-Property Inverse Design of Solid-State Materials},
author={Yan Chen and Xueru Wang and Xiaobin Deng and Yilun Liu and Xi Chen and Yunwei Zhang and Lei Wang and Hang Xiao},
year={2024},
eprint={2408.07608},
archivePrefix={arXiv},
primaryClass={cond-mat.mtrl-sci},
url={https://arxiv.org/abs/2408.07608},
}
```
## Contact
For any questions or suggestions, please contact us at [[email protected]](mailto:[email protected]).
---
We hope that the MatterGPT Demo will inspire and assist you in your material design and research work. Enjoy using it! |