metadata

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

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

CIF to SLICES Conversion
SLICES to CIF Conversion
Structure Visualization
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}, 
}