Universal machine learning interatomic potentials are ready for phonons
Abstract There has been an ongoing race for the past several years to develop the best universal machine learning interatomic potential. This progress has led to increasingly accurate models for predicting energy, forces, and stresses, combining innovative architectures with big data. Here, we bench...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01650-1 |
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| author | Antoine Loew Dewen Sun Hai-Chen Wang Silvana Botti Miguel A. L. Marques |
| author_facet | Antoine Loew Dewen Sun Hai-Chen Wang Silvana Botti Miguel A. L. Marques |
| author_sort | Antoine Loew |
| collection | DOAJ |
| description | Abstract There has been an ongoing race for the past several years to develop the best universal machine learning interatomic potential. This progress has led to increasingly accurate models for predicting energy, forces, and stresses, combining innovative architectures with big data. Here, we benchmark these models on their ability to predict harmonic phonon properties, which are critical for understanding the vibrational and thermal behavior of materials. Using around 10 000 ab initio phonon calculations, we evaluate model performance across various phonon-related parameters to test the universal applicability of these models. The results reveal that some models achieve high accuracy in predicting harmonic phonon properties. However, others still exhibit substantial inaccuracies, even if they excel in the prediction of the energy and the forces for materials close to dynamical equilibrium. These findings highlight the importance of considering phonon-related properties in the development of universal machine learning interatomic potentials. |
| format | Article |
| id | doaj-art-182b0b5e787c4e0695981b0a452db577 |
| institution | DOAJ |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-182b0b5e787c4e0695981b0a452db5772025-08-20T03:21:03ZengNature Portfolionpj Computational Materials2057-39602025-06-011111810.1038/s41524-025-01650-1Universal machine learning interatomic potentials are ready for phononsAntoine Loew0Dewen Sun1Hai-Chen Wang2Silvana Botti3Miguel A. L. Marques4Research Center Future Energy Materials and Systems of the University Alliance Ruhr and Interdisciplinary Centre for Advanced Materials Simulation, Ruhr University Bochum, Universitätsstraße 150Research Center Future Energy Materials and Systems of the University Alliance Ruhr and Interdisciplinary Centre for Advanced Materials Simulation, Ruhr University Bochum, Universitätsstraße 150Research Center Future Energy Materials and Systems of the University Alliance Ruhr and Interdisciplinary Centre for Advanced Materials Simulation, Ruhr University Bochum, Universitätsstraße 150Research Center Future Energy Materials and Systems of the University Alliance Ruhr and Interdisciplinary Centre for Advanced Materials Simulation, Ruhr University Bochum, Universitätsstraße 150Research Center Future Energy Materials and Systems of the University Alliance Ruhr and Interdisciplinary Centre for Advanced Materials Simulation, Ruhr University Bochum, Universitätsstraße 150Abstract There has been an ongoing race for the past several years to develop the best universal machine learning interatomic potential. This progress has led to increasingly accurate models for predicting energy, forces, and stresses, combining innovative architectures with big data. Here, we benchmark these models on their ability to predict harmonic phonon properties, which are critical for understanding the vibrational and thermal behavior of materials. Using around 10 000 ab initio phonon calculations, we evaluate model performance across various phonon-related parameters to test the universal applicability of these models. The results reveal that some models achieve high accuracy in predicting harmonic phonon properties. However, others still exhibit substantial inaccuracies, even if they excel in the prediction of the energy and the forces for materials close to dynamical equilibrium. These findings highlight the importance of considering phonon-related properties in the development of universal machine learning interatomic potentials.https://doi.org/10.1038/s41524-025-01650-1 |
| spellingShingle | Antoine Loew Dewen Sun Hai-Chen Wang Silvana Botti Miguel A. L. Marques Universal machine learning interatomic potentials are ready for phonons npj Computational Materials |
| title | Universal machine learning interatomic potentials are ready for phonons |
| title_full | Universal machine learning interatomic potentials are ready for phonons |
| title_fullStr | Universal machine learning interatomic potentials are ready for phonons |
| title_full_unstemmed | Universal machine learning interatomic potentials are ready for phonons |
| title_short | Universal machine learning interatomic potentials are ready for phonons |
| title_sort | universal machine learning interatomic potentials are ready for phonons |
| url | https://doi.org/10.1038/s41524-025-01650-1 |
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