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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 | |
|  | |
| ## 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}, | |
| } | |
| ``` |