EperLuo/PubChemQH

Introduction

Welcome! This is the dataset used in the ICML'25 paper Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity and ICLR'24 paper Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonian for Scalable Molecular Systems.

For more details, please refer to the code repository. If you need to use the PubChem QH dataset, please cite these two articles.

Citation

@InProceedings{pmlr-v267-luo25l,
  title = 	 {Efficient and Scalable Density Functional Theory {H}amiltonian Prediction through Adaptive Sparsity},
  author =       {Luo, Erpai and Wei, Xinran and Huang, Lin and Li, Yunyang and Yang, Han and Xia, Zaishuo and Wang, Zun and Liu, Chang and Shao, Bin and Zhang, Jia},
  booktitle = 	 {Proceedings of the 42nd International Conference on Machine Learning},
  pages = 	 {41368--41390},
  year = 	 {2025},
  volume = 	 {267},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {13--19 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://raw.githubusercontent.com/mlresearch/v267/main/assets/luo25l/luo25l.pdf},
  url = 	 {https://proceedings.mlr.press/v267/luo25l.html}
}

@inproceedings{
li2025enhancing,
title={Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems},
author={Yunyang Li and Zaishuo Xia and Lin Huang and Xinran Wei and Samuel Harshe and Han Yang and Erpai Luo and Zun Wang and Jia Zhang and Chang Liu and Bin Shao and Mark Gerstein},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=twEvvkQqPS}
}