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# 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.