dataset/README.md

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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:

@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, used under CC BY-SA 3.0.

Our modified version, the SCFBench dataset, is also licensed under CC BY-SA 3.0.

About ByteDance Seed Team

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.