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

Arslan Mazitov (), Filippo Bigi, Matthias Kellner, Paolo Pegolo, Davide Tisi, Guillaume Fraux, Sergey Pozdnyakov, Philip Loche and Michele Ceriotti ()
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Arslan Mazitov: École Polytechnique Fédérale de Lausanne, Laboratory of Computational Science and Modeling, Institut des Matériaux
Filippo Bigi: École Polytechnique Fédérale de Lausanne, Laboratory of Computational Science and Modeling, Institut des Matériaux
Matthias Kellner: École Polytechnique Fédérale de Lausanne, Laboratory of Computational Science and Modeling, Institut des Matériaux
Paolo Pegolo: École Polytechnique Fédérale de Lausanne, Laboratory of Computational Science and Modeling, Institut des Matériaux
Davide Tisi: École Polytechnique Fédérale de Lausanne, Laboratory of Computational Science and Modeling, Institut des Matériaux
Guillaume Fraux: École Polytechnique Fédérale de Lausanne, Laboratory of Computational Science and Modeling, Institut des Matériaux
Sergey Pozdnyakov: École Polytechnique Fédérale de Lausanne, Laboratory of Computational Science and Modeling, Institut des Matériaux
Philip Loche: École Polytechnique Fédérale de Lausanne, Laboratory of Computational Science and Modeling, Institut des Matériaux
Michele Ceriotti: École Polytechnique Fédérale de Lausanne, Laboratory of Computational Science and Modeling, Institut des Matériaux

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Machine-learning interatomic potentials have greatly extended the reach of atomic-scale simulations, offering the accuracy of first-principles calculations at a fraction of the cost. Leveraging large quantum mechanical databases and expressive architectures, recent universal models deliver qualitative accuracy across the periodic table but are often biased toward low-energy configurations. We introduce PET-MAD, a generally applicable interatomic potential trained on a dataset combining stable inorganic and organic solids, systematically modified to enhance atomic diversity. Using a moderate but thoroughly consistent level of electronic-structure theory, we assess PET-MAD’s accuracy on established benchmarks and advanced simulations of six materials. Despite the small training set and lightweight architecture, PET-MAD is competitive with the state-of-the-art machine-learned interatomic potentials for inorganic solids, while also being reliable for molecules, organic materials, and surfaces. It is stable and fast, enabling the near-quantitative study of thermal and quantum mechanical fluctuations, functional properties, and phase transitions out of the box. It can be efficiently fine-tuned to deliver full quantum mechanical accuracy with a minimal number of targeted calculations.

Date: 2025
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DOI: 10.1038/s41467-025-65662-7

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