Papers

A list of selected publications using the metatensor ecosystem and its components.

If you use one of the metatensor tools for your research, please contact us so we can add your publication to this list (or open a pull request to the metatensor pages repository).

General reference for metatensor and metatomic

  1. Bigi, F., Abbott, J. W., Loche, P., Mazitov, A., Tisi, D., Langer, M. F., Goscinski, A., Pegolo, P., Chong, S., Goswami, R., Chorna, S., Kellner, M., Ceriotti, M., & Fraux, G. (2025). Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning. arXiv. https://doi.org/10.48550/arxiv.2508.15704

2025

  1. Chong, S., Bigi, F., Grasselli, F., Loche, P., Kellner, M., & Ceriotti, M. (2025). Prediction rigidities for data-driven chemistry. Faraday Discussions, 256, 322–344. https://doi.org/10.1039/d4fd00101j
  2. Loche, P., Huguenin-Dumittan, K. K., Honarmand, M., Xu, Q., Rumiantsev, E., How, W. B., Langer, M. F., & Ceriotti, M. (2025). Fast and flexible long-range models for atomistic machine learning. The Journal of Chemical Physics, 162(14). https://doi.org/10.1063/5.0251713
  3. Suman, D., Nigam, J., Saade, S., Pegolo, P., Türk, H., Zhang, X., Chan, G. K.-L., & Ceriotti, M. (2025). Exploring the Design Space of Machine Learning Models for Quantum Chemistry with a Fully Differentiable Framework. Journal of Chemical Theory and Computation, 21(13), 6505–6516. https://doi.org/10.1021/acs.jctc.5c00522
  4. Kellner, M., Holmes, J. B., Rodriguez-Madrid, R., Viscosi, F., Zhang, Y., Emsley, L., & Ceriotti, M. (2025). A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy. The Journal of Physical Chemistry Letters, 16(34), 8714–8722. https://doi.org/10.1021/acs.jpclett.5c01819
  5. Türk, H., Tisi, D., & Ceriotti, M. (2025). Reconstructions and Dynamics of β-Lithium Thiophosphate Surfaces. PRX Energy, 4(3). https://doi.org/10.1103/5hf9-hlj6
  6. How, W. B., Febrer, P., Chong, S., Mazitov, A., Bigi, F., Kellner, M., Pozdnyakov, S., & Ceriotti, M. (2025). A universal machine learning model for the electronic density of states. arXiv. https://doi.org/10.48550/arxiv.2508.17418
  7. Bigi, F., & Ceriotti, M. (2025). Learning the action for long-time-step simulations of molecular dynamics. arXiv. https://doi.org/10.48550/arxiv.2508.01068
  8. Mazitov, A., Bigi, F., Kellner, M., Pegolo, P., Tisi, D., Fraux, G., Pozdnyakov, S., Loche, P., & Ceriotti, M. (2025). PET-MAD, a lightweight universal interatomic potential for advanced materials modeling. arXiv. https://doi.org/10.48550/arxiv.2503.14118
  9. How, W. B., Chong, S., Grasselli, F., Huguenin-Dumittan, K. K., & Ceriotti, M. (2024). Adaptive energy reference for machine-learning models of the electronic density of states. https://doi.org/10.48550/arxiv.2407.01068

2024

  1. Nigam, J., Pozdnyakov, S. N., Huguenin-Dumittan, K. K., & Ceriotti, M. (2024). Completeness of atomic structure representations. APL Machine Learning, 2(1). https://doi.org/10.1063/5.0160740
  2. Cignoni, E., Suman, D., Nigam, J., Cupellini, L., Mennucci, B., & Ceriotti, M. (2024). Electronic Excited States from Physically Constrained Machine Learning. ACS Central Science, 10(3), 637–648. https://doi.org/10.1021/acscentsci.3c01480
  3. Mazitov, A., Springer, M. A., Lopanitsyna, N., Fraux, G., De, S., & Ceriotti, M. (2024). Surface segregation in high-entropy alloys from alchemical machine learning. Journal of Physics: Materials, 7(2), 025007. https://doi.org/10.1088/2515-7639/ad2983
  4. Kellner, M., & Ceriotti, M. (2024). Uncertainty quantification by direct propagation of shallow ensembles. Machine Learning: Science and Technology, 5(3), 035006. https://doi.org/10.1088/2632-2153/ad594a
  5. Bigi, F., Chong, S., Ceriotti, M., & Grasselli, F. (2024). A prediction rigidity formalism for low-cost uncertainties in trained neural networks. Machine Learning: Science and Technology, 5(4), 045018. https://doi.org/10.1088/2632-2153/ad805f

2023

  1. Chong, S., Grasselli, F., Ben Mahmoud, C., Morrow, J. D., Deringer, V. L., & Ceriotti, M. (2023). Robustness of Local Predictions in Atomistic Machine Learning Models. Journal of Chemical Theory and Computation, 19(22), 8020–8031. https://doi.org/10.1021/acs.jctc.3c00704
  2. Huguenin-Dumittan, K. K., Loche, P., Haoran, N., & Ceriotti, M. (2023). Physics-Inspired Equivariant Descriptors of Nonbonded Interactions. The Journal of Physical Chemistry Letters, 14(43), 9612–9618. https://doi.org/10.1021/acs.jpclett.3c02375
  3. Lopanitsyna, N., Fraux, G., Springer, M. A., De, S., & Ceriotti, M. (2023). Modeling high-entropy transition metal alloys with alchemical compression. Physical Review Materials, 7(4). https://doi.org/10.1103/physrevmaterials.7.045802