refs.bib

@article{behler_generalized_2007,
  title = {Generalized {{Neural-Network Representation}} of {{High-Dimensional Potential-Energy Surfaces}}},
  author = {Behler, J{\"o}rg and Parrinello, Michele},
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  publisher = {{American Physical Society}},
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  urldate = {2023-08-03}
}

@article{bartok_representing_2013,
  title = {On Representing Chemical Environments},
  author = {Bart{\'o}k, Albert P. and Kondor, Risi and Cs{\'a}nyi, G{\'a}bor},
  year = {2013},
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  journal = {Phys. Rev. B},
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  number = {18},
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  publisher = {{American Physical Society}},
  doi = {10.1103/PhysRevB.87.184115},
  urldate = {2022-02-24}
}

@article{willatt_feature_2018,
  title = {Feature Optimization for Atomistic Machine Learning Yields a Data-Driven Construction of the Periodic Table of the Elements},
  author = {Willatt, Michael J. and Musil, F{\'e}lix and Ceriotti, Michele},
  year = {2018},
  month = dec,
  journal = {Phys. Chem. Chem. Phys.},
  volume = {20},
  number = {47},
  pages = {29661--29668},
  publisher = {{The Royal Society of Chemistry}},
  issn = {1463-9084},
  doi = {10.1039/C8CP05921G},
  urldate = {2024-02-19},
  langid = {english}
}

@article{bigi_smooth_2022,
    author = {Bigi, Filippo and Huguenin-Dumittan, Kevin K. and Ceriotti, Michele and Manolopoulos, David E.},
    title = "{A smooth basis for atomistic machine learning}",
    journal = {The Journal of Chemical Physics},
    volume = {157},
    number = {23},
    pages = {234101},
    year = {2022},
    month = {12},
    issn = {0021-9606},
    doi = {10.1063/5.0124363},
    url = {https://doi.org/10.1063/5.0124363},
}

@article{pozdnyakov_smooth_2023,
  title = {Smooth, Exact Rotational Symmetrization for Deep Learning on Point Clouds},
  author = {Pozdnyakov, Sergey N. and Ceriotti, Michele},
  year = {2023},
  month = may,
  journal = {arXiv.org},
  urldate = {2025-01-24},
  howpublished = {https://arxiv.org/abs/2305.19302v3},
  langid = {english}
}

@article{bartok_gaussian_2010,
  title = {Gaussian {{Approximation Potentials}}: {{The Accuracy}} of {{Quantum Mechanics}}, without the {{Electrons}}},
  shorttitle = {Gaussian {{Approximation Potentials}}},
  author = {Bart{\'o}k, Albert P. and Payne, Mike C. and Kondor, Risi and Cs{\'a}nyi, G{\'a}bor},
  year = {2010},
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  urldate = {2023-08-03}
}

@article{bigi_mlst_2024,
  title = {A prediction rigidity formalism for low-cost uncertainties in trained neural networks},
  author = {Bigi, Filippo and Chong, Sanggyu and Ceriotti, Michele and Grasselli, Federico},
  year = {2024},
  journal = {Machine Learn.: Sci. Technol.},
  volume = {5},
  number = {4},
  pages = {045018},
  doi = {10.1088/2632-2153/ad805f},
}

@article{bigi_flashmd_2025,
  title={FlashMD: long-stride, universal prediction of molecular dynamics},
  author={Bigi, Filippo and Chong, Sanggyu and Kristiadi, Agustinus and Ceriotti, Michele},
  journal={arXiv preprint arXiv:2505.19350},
  year={2025}
}

@article{batatia2022mace,
  title={MACE: Higher order equivariant message passing neural networks for fast and accurate force fields},
  author={Batatia, Ilyes and Kovacs, David P and Simm, Gregor and Ortner, Christoph and Cs{\'a}nyi, G{\'a}bor},
  journal={Advances in neural information processing systems},
  volume={35},
  pages={11423--11436},
  year={2022}
}

@misc{dpa3_2025,
  title={A Graph Neural Network for the Era of Large Atomistic Models},
  author={Zhang, Duo and Peng, Anyang and Cai, Chun and Li, Wentao and Zhou, Yuanchang and Zeng, Jinzhe and Guo, Mingyu and Zhang, Chengqian and Li, Bowen and Jiang, Hong and Zhu, Tong and Jia, Weile and Zhang, Linfeng and Wang, Han},
  year={2025},
  eprint={2506.01686},
  archivePrefix={arXiv},
  primaryClass={physics.chem-ph},
  doi={10.48550/arXiv.2506.01686}
}

@article{domina2025representing,
  title={Representing spherical tensors with scalar-based machine-learning models},
  author={Domina, Michelangelo and Bigi, Filippo and Pegolo, Paolo and Ceriotti, Michele},
  journal={The Journal of Chemical Physics},
  volume={163},
  number={16},
  year={2025},
  publisher={AIP Publishing}
}

@article{malosso2026transferable,
  title={Transferable machine learning of excited-state dynamics with extremal pooling},
  author={Malosso, Cesare and How, Wei Bin and Mir{\'o}n, Gonzalo D{\'\i}az and Hassanali, Ali and Ceriotti, Michele},
  journal={arXiv preprint arXiv:2606.16859},
  year={2026}
}