Fully Integrating Atomistic Modeling with Machine Learning

Project Information

FIAMMA

Grant agreement ID: 101001890

DOI

10.3030/101001890

EC signature date 18 December 2020

Start date 1 January 2022

End date 31 December 2026

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 998 015,00

EU contribution

€ 1 998 015,00

Coordinated by

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Switzerland

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Publications

Peer reviewed articles (30)

Fast evaluation of spherical harmonics with sphericart

Author(s): F. Bigi, G. Fraux, N. J. Browning, and M. Ceriotti
Published in: Journal of Chemical Physics, Issue 159, 2023, Page(s) 064802, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/5.0156307

Representing spherical tensors with scalar-based machine-learning models

Author(s): M. Domina, F. Bigi, P. Pegolo, M. Ceriotti
Published in: The Journal of Chemical Physics, Issue 163, 2025, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/5.0284802

A prediction rigidity formalism for low-cost uncertainties in trained neural networks

Author(s): Filippo Bigi, Sanggyu Chong, Michele Ceriotti, Federico Grasselli
Published in: Machine Learning: Science and Technology, Issue 5, 2024, Page(s) 045018, ISSN 2632-2153
Publisher: IOP
DOI: 10.1088/2632-2153/ad805f

Prediction rigidities for data-driven chemistry

Author(s): Sanggyu Chong, Filippo Bigi, Federico Grasselli, Philip Loche, Matthias Kellner, Michele Ceriotti
Published in: Faraday Discussions, Issue 256, 2026, Page(s) 322-344, ISSN 1359-6640
Publisher: Royal Society of Chemistry
DOI: 10.1039/d4fd00101j

Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

Author(s): Kevin K. Huguenin-Dumittan, Philip Loche, Ni Haoran, Michele Ceriotti
Published in: The Journal of Physical Chemistry Letters, Issue 14, 2023, Page(s) 9612-9618, ISSN 1948-7185
Publisher: American Chemical Society
DOI: 10.1021/acs.jpclett.3c02375

Completeness of atomic structure representations

Author(s): Jigyasa Nigam, Sergey N. Pozdnyakov, Kevin K. Huguenin-Dumittan, Michele Ceriotti
Published in: APL Machine Learning, Issue 2, 2024, ISSN 2770-9019
Publisher: AIP Publishing LLC
DOI: 10.1063/5.0160740

Mechanism of Charge Transport in Lithium Thiophosphate

Author(s): Lorenzo Gigli, Davide Tisi, Federico Grasselli, Michele Ceriotti
Published in: Chemistry of Materials, Issue 36, 2024, Page(s) 1482-1496, ISSN 0897-4756
Publisher: American Chemical Society
DOI: 10.1021/acs.chemmater.3c02726

Unified theory of atom-centered representations and message-passing machine-learning schemes.

Author(s): Jigyasa Nigam; Sergey Pozdnyakov; Guillaume Fraux; Michele Ceriotti
Published in: Journal of Chemical Physics, 2022, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.48550/arxiv.2202.01566

Exploring the Design Space of Machine Learning Models for Quantum Chemistry with a Fully Differentiable Framework

Author(s): Divya Suman, Jigyasa Nigam, Sandra Saade, Paolo Pegolo, Hanna Türk, Xing Zhang, Garnet Kin-Lic Chan, Michele Ceriotti
Published in: Journal of Chemical Theory and Computation, Issue 21, 2025, Page(s) 6505-6516, ISSN 1549-9618
Publisher: American Chemical Society
DOI: 10.1021/acs.jctc.5c00522

Resolving the body-order paradox of machine learning interatomic potentials

Author(s): Sanggyu Chong, Tong Jiang, Michelangelo Domina, Filippo Bigi, Federico Grasselli, Joonho Lee, Michele Ceriotti
Published in: The Journal of Chemical Physics, Issue 164, 2026, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/5.0303302

Predicting hot-electron free energies from ground-state data

Author(s): Chiheb Ben Mahmoud, F. Grasselli, and M. Ceriotti
Published in: Physical Review B, 2022, ISSN 2469-9950
Publisher: American Physical Society
DOI: 10.1103/physrevb.106.l121116

<tt>metatensor</tt> and <tt>metatomic</tt> : Foundational libraries for interoperable atomistic machine learning

Author(s): Filippo Bigi, Joseph W. Abbott, Philip Loche, Arslan Mazitov, Davide Tisi, Marcel F. Langer, Alexander Goscinski, Paolo Pegolo, Sanggyu Chong, Rohit Goswami, Pol Febrer, Sofiia Chorna, Matthias Kellner, Michele Ceriotti, Guillaume Fraux
Published in: The Journal of Chemical Physics, Issue 164, 2026, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/5.0304911

Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties

Author(s): Jigyasa Nigam; Michael J. Willatt; Michele Ceriotti
Published in: Chemical Physics, Issue 1, 2022, Page(s) 156(1), 014115 (2022), ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/5.0072784

Comparing the Latent Features of Universal Machine‐Learning Interatomic Potentials

Author(s): Sofiia Chorna, Davide Tisi, Cesare Malosso, Wei Bin How, Michele Ceriotti, Sanggyu Chong
Published in: Advanced Intelligent Systems, Issue 8, 2026, ISSN 2640-4567
Publisher: Wiley
DOI: 10.1002/aisy.202501497

Pushing the limits of unconstrained machine-learned interatomic potentials

Author(s): Filippo Bigi, Paolo Pegolo, Arslan Mazitov, Jonathan Schmidt, Michele Ceriotti
Published in: Machine Learning: Science and Technology, Issue 7, 2026, Page(s) 035051, ISSN 2632-2153
Publisher: IOP Publishing
DOI: 10.1088/2632-2153/ae6417

A foundation model for atomistic materials chemistry

Author(s): Ilyes Batatia, Philipp Benner, Yuan Chiang, Alin M. Elena, Dávid P. Kovács, Janosh Riebesell, Xavier R. Advincula, Mark Asta, Matthew Avaylon, William J. Baldwin, Fabian Berger, Noam Bernstein, Arghya Bhowmik, Filippo Bigi, Samuel M. Blau, Vlad Cărare, Michele Ceriotti, Sanggyu Chong, James P. Darby, Sandip De, Flaviano Della Pia, Volker L. Deringer, Rokas Elijošius, Zakariya El-Machachi, Edvi
Published in: The Journal of Chemical Physics, Issue 163, 2025, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/5.0297006

A universal machine learning model for the electronic density of states

Author(s): Wei Bin How, Pol Febrer, Sanggyu Chong, Arslan Mazitov, Filippo Bigi, Matthias Kellner, Sergey Pozdnyakov, Michele Ceriotti
Published in: Digital Discovery, Issue 5, 2026, Page(s) 1635-1649, ISSN 2635-098X
Publisher: Royal Society of Chemistry (RSC)
DOI: 10.1039/d5dd00557d

Fast and flexible long-range models for atomistic machine learning

Author(s): Philip Loche, Kevin K. Huguenin-Dumittan, Melika Honarmand, Qianjun Xu, Egor Rumiantsev, Wei Bin How, Marcel F. Langer, Michele Ceriotti
Published in: The Journal of Chemical Physics, Issue 162, 2025, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/5.0251713

Probing the effects of broken symmetries in machine learning

Author(s): Marcel F Langer, Sergey N Pozdnyakov, Michele Ceriotti
Published in: Machine Learning: Science and Technology, Issue 5, 2024, Page(s) 04LT01, ISSN 2632-2153
Publisher: IOP
DOI: 10.1088/2632-2153/ad86a0

Robustness of Local Predictions in Atomistic Machine Learning Models

Author(s): Sanggyu Chong, Federico Grasselli, Chiheb Ben Mahmoud, Joe D. Morrow, Volker L. Deringer, Michele Ceriotti
Published in: Journal of Chemical Theory and Computation, Issue 19, 2023, Page(s) 8020-8031, ISSN 1549-9618
Publisher: American Chemical Society
DOI: 10.1021/acs.jctc.3c00704

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

Author(s): Arslan Mazitov, Filippo Bigi, Matthias Kellner, Paolo Pegolo, Davide Tisi, Guillaume Fraux, Sergey Pozdnyakov, Philip Loche, Michele Ceriotti
Published in: Nature Communications, Issue 16, 2025, ISSN 2041-1723
Publisher: Nature Publishing Group
DOI: 10.1038/s41467-025-65662-7

Local invertibility and sensitivity of atomic structure-feature mappings

Author(s): Sergey Pozdnyakov; Liwei Zhang; Christoph Ortner; Gábor Csányi; Michele Ceriotti
Published in: Open Research Europe, Issue 1, 2021, ISSN 2732-5121
Publisher: Excellent Science
DOI: 10.12688/openreseurope.14156.1

Electronic Excited States from Physically Constrained Machine Learning

Author(s): Edoardo Cignoni, Divya Suman, Jigyasa Nigam, Lorenzo Cupellini, Benedetta Mennucci, Michele Ceriotti
Published in: ACS Central Science, Issue 10, 2024, Page(s) 637-648, ISSN 2374-7943
Publisher: American Chemical Society
DOI: 10.1021/acscentsci.3c01480

scikit-matter : A Suite of Generalisable Machine Learning Methods Born out of Chemistry and Materials Science

Author(s): Alexander Goscinski, Victor Paul Principe, Guillaume Fraux, Sergei Kliavinek, Benjamin Aaron Helfrecht, Philip Loche, Michele Ceriotti, Rose Kathleen Cersonsky
Published in: Open Research Europe, Issue 3, 2024, Page(s) 81, ISSN 2732-5121
Publisher: London, UK: F1000 Research Limited
DOI: 10.12688/openreseurope.15789.2

Electronic-Structure Properties from Atom-Centered Predictions of the Electron Density

Author(s): Andrea Grisafi; Alan M. Lewis; Mariana Rossi; Michele Ceriotti
Published in: Journal of Chemical Theory and Computation, Issue 19, 2023, Page(s) 4451, ISSN 1549-9618
Publisher: American Chemical Society
DOI: 10.1021/acs.jctc.2c00850

A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy

Author(s): Matthias Kellner, Jacob B. Holmes, Ruben Rodriguez-Madrid, Florian Viscosi, Yuxuan Zhang, Lyndon Emsley, Michele Ceriotti
Published in: The Journal of Physical Chemistry Letters, Issue 16, 2025, Page(s) 8714-8722, ISSN 1948-7185
Publisher: American Chemical Society
DOI: 10.1021/acs.jpclett.5c01819

Adaptive energy reference for machine-learning models of the electronic density of states

Author(s): Wei Bin How, Sanggyu Chong, Federico Grasselli, Kevin K. Huguenin-Dumittan, Michele Ceriotti
Published in: Physical Review Materials, Issue 9, 2025, ISSN 2475-9953
Publisher: APS
DOI: 10.1103/physrevmaterials.9.013802

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

Author(s): Yair Litman, Venkat Kapil, Yotam M. Y. Feldman, Davide Tisi, Tomislav Begušić, Karen Fidanyan, Guillaume Fraux, Jacob Higer, Matthias Kellner, Tao E. Li, Eszter S. Pós, Elia Stocco, George Trenins, Barak Hirshberg, Mariana Rossi, Michele Ceriotti
Published in: The Journal of Chemical Physics, Issue 161, 2024, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/5.0215869

"Thermal conductivity of <mml:math xmlns:mml=""http://www.w3.org/1998/Math/MathML""><mml:mrow><mml:msub><mml:mi>Li</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:msub><mml:mi>PS</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:mrow></mml:math> solid electrolytes with <i>ab initio</i> accuracy"

Author(s): Davide Tisi, Federico Grasselli, Lorenzo Gigli, Michele Ceriotti
Published in: Physical Review Materials, Issue 8, 2024, ISSN 2475-9953
Publisher: ACS
DOI: 10.1103/physrevmaterials.8.065403

Ranking the synthesizability of hypothetical zeolites with the sorting hat

Author(s): B. A. Helfrecht, G. Pireddu, R. Semino, S. M. Auerbach, and M. Ceriotti
Published in: Digital Discovery, Issue 1, 2022, Page(s) 779, ISSN 2635-098X
Publisher: Royal Society of Chemistry
DOI: 10.1039/d2dd00056c

Non-peer reviewed articles (2)

A smooth basis for atomistic machine learning

Author(s): F. Bigi, K. K. Huguenin-Dumittan, M. Ceriotti, and D. E. Manolopoulos, J. Chem
Published in: Journal Of Chemical Physics, 2022, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/5.0124363

A data-driven interpretation of the stability of organic molecular crystals

Author(s): R. K. Cersonsky, M. Pakhnova, E. A. Engel, and M. Ceriotti
Published in: Chemical Science, 2023, ISSN 2041-6520
Publisher: Royal Society of Chemistry
DOI: 10.1039/d2sc06198h