2026-05-26T17:35:04Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:h41qv-1n493 2025-09-25T15:17:05Z openaire_data community-mcarchive

Arslan, Mazitov Arslan, Mazitov Filippo, Bigi Matthias, Kellner Paolo, Pegolo Davide, Tisi Guillaume, Fraux Sergey, Pozdnyakov Michele, Ceriotti 2025-09-25 <p>Machine-learning interatomic potentials (MLIPs) 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 MLIP trained on a dataset combining stable inorganic and organic solids, systematically modified to enhance atomic diversity. Using a moderate but highly-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 state-of-the-art MLIPs 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.</p> application/gzip application/gzip application/gzip text/markdown text/plain text/markdown application/gzip application/gzip https://doi.org/10.24435/materialscloud:fe-1p oai:materialscloud.org:h41qv-1n493 mcid:2025.145 eng Materials Cloud https://doi.org/10.48550/arXiv.2503.14118 https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:z2-5f info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode machine learning interatomic potentials foundation models atomistic modeling PET-MAD, a lightweight universal interatomic potential for advanced materials modeling info:eu-repo/semantics/other