Datasets:
ArXiv:
License:
| <div align="center"> | |
| 👋 Hi, everyone! | |
| <br> | |
| We are <b>ByteDance Seed team.</b> | |
| </div> | |
| <p align="center"> | |
| You can get to know us better through the following channels👇 | |
| <br> | |
| <a href="https://seed.bytedance.com/"> | |
| <img src="https://img.shields.io/badge/Website-%231e37ff?style=for-the-badge&logo=bytedance&logoColor=white"></a> | |
| <a href="https://github.com/user-attachments/assets/5793e67c-79bb-4a59-811a-fcc7ed510bd4"> | |
| <img src="https://img.shields.io/badge/WeChat-07C160?style=for-the-badge&logo=wechat&logoColor=white"></a> | |
| <a href="https://www.xiaohongshu.com/user/profile/668e7e15000000000303157d?xsec_token=ABl2-aqekpytY6A8TuxjrwnZskU-6BsMRE_ufQQaSAvjc%3D&xsec_source=pc_search"> | |
| <img src="https://img.shields.io/badge/Xiaohongshu-%23FF2442?style=for-the-badge&logo=xiaohongshu&logoColor=white"></a> | |
| <a href="https://www.zhihu.com/org/dou-bao-da-mo-xing-tuan-dui/"> | |
| <img src="https://img.shields.io/badge/zhihu-%230084FF?style=for-the-badge&logo=zhihu&logoColor=white"></a> | |
| </p> | |
|  | |
| # Towards A Universally Transferable Acceleration Method for Density Functional Theory | |
| Zhe Liu, Yuyan Ni, Zhichen Pu, Qiming Sun, Siyuan Liu & Wen Yan | |
| https://arxiv.org/abs/2509.25724 | |
| # Citing SCFBench | |
| If you use SCFBench in your research, please cite: | |
| ```latex | |
| @misc{liu2025universallytransferableaccelerationmethod, | |
| title={Towards A Universally Transferable Acceleration Method for Density Functional Theory}, | |
| author={Zhe Liu and Yuyan Ni and Zhichen Pu and Qiming Sun and Siyuan Liu and Wen Yan}, | |
| year={2025}, | |
| eprint={2509.25724}, | |
| archivePrefix={arXiv}, | |
| primaryClass={physics.chem-ph}, | |
| url={https://arxiv.org/abs/2509.25724}, | |
| } | |
| ``` | |
| ## License | |
| The dataset is a derivative of [ChEMBL](https://www.ebi.ac.uk/chembl/), used under [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/). | |
| Our modified version, the SCFBench dataset, is also licensed under [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/). | |
| ## About [ByteDance Seed Team](https://seed.bytedance.com/) | |
| Founded in 2023, ByteDance Seed Team is dedicated to crafting the industry's most advanced AI foundation models. The team aspires to become a world-class research team and make significant contributions to the advancement of science and society. | |