How to cite¶

If you find our models useful, please cite the corresponding articles.

PET-MAD-1.5¶

@misc{PET-MAD-1.5-2026,
      title={High-quality, high-information datasets for universal atomistic machine learning},
      author={Cesare Malosso and Filippo Bigi and Paolo Pegolo and Joseph W. Abbott and Philip Loche and Mariana Rossi and Michele Ceriotti and Arslan Mazitov},
      year={2026},
      eprint={2603.02089},
      archivePrefix={arXiv},
      primaryClass={cond-mat.mtrl-sci},
      url={https://arxiv.org/abs/2603.02089},
}

Current UPET architecture — PET-OAM, PET-OMat, PET-OMAD, PET-OMATPES, PET-SPICE¶

@misc{pushing-unconstrained-2026,
      title={Pushing the limits of unconstrained machine-learned interatomic potentials},
      author={Filippo Bigi and Paolo Pegolo and Arslan Mazitov and Michele Ceriotti},
      year={2026},
      eprint={2601.16195},
      archivePrefix={arXiv},
      primaryClass={physics.chem-ph},
      url={https://arxiv.org/abs/2601.16195},
}

PET-MAD¶

@misc{PET-MAD-2025,
      title={PET-MAD as a lightweight universal interatomic potential for advanced materials modeling},
      author={Mazitov, Arslan and Bigi, Filippo and Kellner, Matthias and Pegolo, Paolo and Tisi, Davide and Fraux, Guillaume and Pozdnyakov, Sergey and Loche, Philip and Ceriotti, Michele},
      journal={Nature Communications},
      volume={16},
      number={1},
      pages={10653},
      year={2025},
      url={https://doi.org/10.1038/s41467-025-65662-7},
}

PET-MAD-DOS¶

@misc{PET-MAD-DOS-2025,
      title={A universal machine learning model for the electronic density of states},
      author={Wei Bin How and Pol Febrer and Sanggyu Chong and Arslan Mazitov and Filippo Bigi and Matthias Kellner and Sergey Pozdnyakov and Michele Ceriotti},
      year={2025},
      eprint={2508.17418},
      archivePrefix={arXiv},
      primaryClass={physics.chem-ph},
      url={https://arxiv.org/abs/2508.17418},
}

PET architecture (general)¶

@misc{PET-ECSE-2023,
  title = {Smooth, Exact Rotational Symmetrization for Deep Learning on Point Clouds},
  journal = {Advances in {{Neural Information Processing Systems}}},
  author = {Pozdnyakov, Sergey and Ceriotti, Michele},
  year = 2023,
  volume = {36},
  pages = {79469--79501},
}