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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2057-3960 |