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SLICES
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---
title: SLICES
emoji: 🏢
colorFrom: red
colorTo: purple
sdk: gradio
sdk_version: 4.41.0
app_file: app.py
pinned: false
license: lgpl-2.1
thumbnail: >-
  https://cdn-uploads.huggingface.co/production/uploads/66ae0d46f0bb529189cd3ecf/Wb4YnKHtmUi1bNgmHM4Mz.png
short_description: CIF2SLICES, SLICES2CIF
---

# Crystal Structure and SLICES Converter
![SLICES Demo](1.png)
## Description

This application provides a user-friendly interface for converting between crystallographic information files (CIF) and SLICES (Simplified Line-Input Crystal-Encoding System) representations. It also includes features for SLICES augmentation and canonicalization.

SLICES is a text-based encoding of crystal structures that allows for efficient manipulation and generation of new materials.

## Features

1. CIF to SLICES Conversion
2. SLICES to CIF Conversion
3. Structure Visualization
4. SLICES Augmentation and Canonicalization

## Functionality

### CIF to SLICES Conversion
- Upload a CIF file or use the default "NdSiRu.cif".
- Click "Convert CIF to SLICES" to generate the SLICES representation.
- The resulting SLICES string will be displayed and automatically copied to the SLICES input fields.

### SLICES to CIF Conversion
- Enter a SLICES string in the input field.
- Click "Convert SLICES to CIF" to generate the CIF file.
- The resulting CIF file can be downloaded, and the structure will be visualized.

### Structure Visualization
- Both original and converted structures are displayed as images.
- Structures are automatically wrapped and converted to primitive cells for consistency.

### SLICES Augmentation and Canonicalization
- Enter a SLICES string in the input field.
- Adjust the number of augmentations using the slider.
- Click "Augment and Canonicalize" to generate augmented and canonical SLICES strings.

## Citation
```
@article{xiao2023invertible,
  title={An invertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning},
  author={Xiao, Hang and Li, Rong and Shi, Xiaoyang and Chen, Yan and Zhu, Liangliang and Chen, Xi and Wang, Lei},
  journal={Nature Communications},
  volume={14},
  number={1},
  pages={7027},
  year={2023},
  publisher={Nature Publishing Group UK London}
}
@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}, 
}
```