PET-MAD, a lightweight universal interatomic potential for advanced materials modeling
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916880522936320 |
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| author | Mazitov, Arslan Bigi, Filippo Kellner, Matthias Pegolo, Paolo Tisi, Davide Fraux, Guillaume Pozdnyakov, Sergey Loche, Philip Ceriotti, Michele |
| author_facet | Mazitov, Arslan Bigi, Filippo Kellner, Matthias Pegolo, Paolo Tisi, Davide Fraux, Guillaume Pozdnyakov, Sergey Loche, Philip Ceriotti, Michele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14118 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PET-MAD, a lightweight universal interatomic potential for advanced materials modeling Mazitov, Arslan Bigi, Filippo Kellner, Matthias Pegolo, Paolo Tisi, Davide Fraux, Guillaume Pozdnyakov, Sergey Loche, Philip Ceriotti, Michele Materials Science Machine Learning Chemical Physics 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. |
| title | PET-MAD, a lightweight universal interatomic potential for advanced materials modeling |
| topic | Materials Science Machine Learning Chemical Physics |
| url | https://arxiv.org/abs/2503.14118 |