Update README.md

README.md

@@ -11,3 +11,61 @@ license: lgpl-2.1
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 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}, 
+}
+```