.. _cite:

How to cite
===========

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

PET-MAD-1.5
-----------

.. code-block:: bibtex

   @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
-------------------------------------------------------------------------------

.. code-block:: bibtex

   @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
-------

.. code-block:: bibtex

   @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
-----------

.. code-block:: bibtex

   @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)
--------------------------

.. code-block:: bibtex

   @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},
   }