publications

2026

1. arXiv

   Machine-Learned NMR Shieldings in Molecular Solids with Built-In Hybrid-Functional Molecular Corrections

   Matthias Kellner, Ruben Rodriguez-Madrid, Jacob B Holmes, and 4 more authors

   arXiv preprint, 2026

   arXiv Bib Code Website

   @article{kellner2026machine,
     title = {Machine-Learned NMR Shieldings in Molecular Solids with Built-In Hybrid-Functional Molecular Corrections},
     author = {Kellner, Matthias and Rodriguez-Madrid, Ruben and Holmes, Jacob B and Unzueta, Pablo A and Beran, Gregory JO and Emsley, Lyndon and Ceriotti, Michele},
     journal = {arXiv preprint},
     year = {2026},
   }

2. arXiv

   Errors that matter: Uncertainty-aware universal machine-learning potentials calibrated on experiments

   Matthias Kellner, Teitur Hansen, Thomas Bligaard, and 2 more authors

   arXiv preprint, 2026

   arXiv Bib

   @article{kellner2026errors,
     title = {Errors that matter: Uncertainty-aware universal machine-learning potentials calibrated on experiments},
     author = {Kellner, Matthias and Hansen, Teitur and Bligaard, Thomas and Jacobsen, Karsten Wedel and Ceriotti, Michele},
     journal = {arXiv preprint},
     year = {2026},
   }

3. arXiv

   Quantum-corrected NMR crystallography at scale

   Matthias Kellner, Ruben Rodriguez-Madrid, Jacob B Holmes, and 3 more authors

   arXiv preprint, 2026

   arXiv Bib

   @article{kellner2026quantum,
     title = {Quantum-corrected NMR crystallography at scale},
     author = {Kellner, Matthias and Rodriguez-Madrid, Ruben and Holmes, Jacob B and Principe, Victor Paul and Emsley, Lyndon and Ceriotti, Michele},
     journal = {arXiv preprint},
     year = {2026},
   }

4. JCTC

   How to Train a Shallow Ensemble

   Moritz Schäfer, Matthias Kellner, Johannes Kästner, and 1 more author

   Journal of Chemical Theory and Computation, 2026

   DOI Bib

   @article{schafer2026train,
     title = {How to Train a Shallow Ensemble},
     author = {Sch{\"a}fer, Moritz and Kellner, Matthias and K{\"a}stner, Johannes and Ceriotti, Michele},
     journal = {Journal of Chemical Theory and Computation},
     volume = {22},
     number = {10},
     pages = {4939--4950},
     year = {2026},
     publisher = {American Chemical Society},
     doi = {10.1021/acs.jctc.6c00310},
   }

5. JCP

   Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning

   Filippo Bigi, Joseph W Abbott, Philip Loche, and 12 more authors

   The Journal of Chemical Physics, 2026

   DOI Bib Code

   @article{bigi2026metatensor,
     title = {Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning},
     author = {Bigi, Filippo and Abbott, Joseph W and Loche, Philip and Mazitov, Arslan and Tisi, Davide and Langer, Marcel F and Goscinski, Alexander and Pegolo, Paolo and Chong, Sanggyu and Goswami, Rohit and Febrer, Pol and Chorna, Sofiia and Kellner, Matthias and Ceriotti, Michele and Fraux, Guillaume},
     journal = {The Journal of Chemical Physics},
     volume = {164},
     number = {6},
     pages = {064113},
     year = {2026},
     publisher = {AIP Publishing},
     doi = {10.1063/5.0304911},
   }

6. Digital Discovery

   A universal machine learning model for the electronic density of states

   Wei Bin How, Pol Febrer, Sanggyu Chong, and 5 more authors

   Digital Discovery, 2026

   DOI Bib

   @article{how2026universal,
     title = {A universal machine learning model for the electronic density of states},
     author = {How, Wei Bin and Febrer, Pol and Chong, Sanggyu and Mazitov, Arslan and Bigi, Filippo and Kellner, Matthias and Pozdnyakov, Sergey and Ceriotti, Michele},
     journal = {Digital Discovery},
     volume = {5},
     number = {4},
     pages = {1635--1649},
     year = {2026},
     publisher = {Royal Society of Chemistry},
     doi = {10.1039/d5dd00557d},
   }

2025

1. Nat. Commun.

   PET-MAD as a lightweight universal interatomic potential for advanced materials modeling

   Arslan Mazitov, Filippo Bigi, Matthias Kellner, and 6 more authors

   Nature Communications, 2025

   DOI Bib Code

   @article{mazitov2025pet,
     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},
     publisher = {Nature Publishing Group},
     doi = {10.1038/s41467-025-65662-7},
   }

2. JPCL

   A deep learning model for chemical shieldings in molecular organic solids including anisotropy

   Matthias Kellner, Jacob B Holmes, Ruben Rodriguez-Madrid, and 4 more authors

   The Journal of Physical Chemistry Letters, 2025

   DOI Bib Code

   @article{kellner2025deep,
     title = {A deep learning model for chemical shieldings in molecular organic solids including anisotropy},
     author = {Kellner, Matthias and Holmes, Jacob B and Rodriguez-Madrid, Ruben and Viscosi, Florian and Zhang, Yuxuan and Emsley, Lyndon and Ceriotti, Michele},
     journal = {The Journal of Physical Chemistry Letters},
     volume = {16},
     number = {34},
     pages = {8714--8722},
     year = {2025},
     publisher = {American Chemical Society},
     doi = {10.1021/acs.jpclett.5c01819},
   }

3. Faraday Discuss.

   Prediction rigidities for data-driven chemistry

   Sanggyu Chong, Filippo Bigi, Federico Grasselli, and 3 more authors

   Faraday Discussions, 2025

   DOI Bib

   @article{chong2025prediction,
     title = {Prediction rigidities for data-driven chemistry},
     author = {Chong, Sanggyu and Bigi, Filippo and Grasselli, Federico and Loche, Philip and Kellner, Matthias and Ceriotti, Michele},
     journal = {Faraday Discussions},
     volume = {256},
     pages = {322--344},
     year = {2025},
     publisher = {Royal Society of Chemistry},
     doi = {10.1039/d4fd00101j},
   }

2024

1. JPCL

   Observation of Transient Prenucleation Species of Calcium Carbonate by DNP-Enhanced NMR

   Martins Balodis, Yu Rao, Gabriele Stevanato, and 5 more authors

   The Journal of Physical Chemistry Letters, 2024

   DOI Bib

   @article{balodis2024observation,
     title = {Observation of Transient Prenucleation Species of Calcium Carbonate by DNP-Enhanced NMR},
     author = {Balodis, Martins and Rao, Yu and Stevanato, Gabriele and Kellner, Matthias and Meibom, Josephine and Negroni, Mattia and Chmelka, Bradley F and Emsley, Lyndon},
     journal = {The Journal of Physical Chemistry Letters},
     volume = {15},
     number = {31},
     pages = {7954--7961},
     year = {2024},
     publisher = {American Chemical Society},
     doi = {10.1021/acs.jpclett.4c01588},
   }

2. MLST

   Uncertainty quantification by direct propagation of shallow ensembles

   Matthias Kellner and Michele Ceriotti

   Machine Learning: Science and Technology, 2024

   DOI Bib Code

   @article{kellner2024uncertainty,
     title = {Uncertainty quantification by direct propagation of shallow ensembles},
     author = {Kellner, Matthias and Ceriotti, Michele},
     journal = {Machine Learning: Science and Technology},
     volume = {5},
     number = {3},
     pages = {035006},
     year = {2024},
     publisher = {IOP Publishing},
     doi = {10.1088/2632-2153/ad594a},
   }

3. JCP

   i-PI 3.0: A flexible and efficient framework for advanced atomistic simulations

   Yair Litman, Venkat Kapil, Yotam MY Feldman, and 13 more authors

   The Journal of Chemical Physics, 2024

   DOI Bib Code

   @article{litman2024pi,
     title = {i-PI 3.0: A flexible and efficient framework for advanced atomistic simulations},
     author = {Litman, Yair and Kapil, Venkat and Feldman, Yotam MY and Tisi, Davide and Begu{\v{s}}i{\'c}, Tomislav and Fidanyan, Karen and Fraux, Guillaume and Higer, Jacob and Kellner, Matthias and Li, Tao E and P{\'o}s, Eszter S and Stocco, Elia and Trenins, George and Hirshberg, Barak and Rossi, Mariana and Ceriotti, Michele},
     journal = {The Journal of Chemical Physics},
     volume = {161},
     number = {6},
     pages = {062504},
     year = {2024},
     publisher = {AIP Publishing},
     doi = {10.1063/5.0215869},
   }