Tensor Learning and Compression of N-phonon Interactions
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915435976327168 |
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| author | Luo, Yao Mangtani, Dhruv Peng, Shiyu Yao, Jia Kliavinek, Sergei Bernardi, Marco |
| author_facet | Luo, Yao Mangtani, Dhruv Peng, Shiyu Yao, Jia Kliavinek, Sergei Bernardi, Marco |
| contents | Phonon interactions from lattice anharmonicity govern thermal properties and heat transport in materials. These interactions are described by n-th order interatomic force constants (n-IFCs), which can be viewed as high-dimensional tensors correlating the motion of n atoms, or equivalently encoding n-phonon scattering processes in momentum space. Here, we introduce a tensor decomposition to efficiently compress n-IFCs for arbitrary order n. Using tensor learning, we find optimal low-rank approximations of n-IFCs by solving the resulting optimization problem. Our approach reveals the inherent low dimensionality of phonon-phonon interactions and allows compression of the 3 and 4-IFC tensors by factors of up to $10^3-10^4$ while retaining high accuracy in calculations of phonon scattering rates and thermal conductivity. Calculations of thermal conductivity using the compressed n-IFCs achieve a speed-up by nearly three orders of magnitude with >98% accuracy relative to the reference uncompressed solution. These calculations include both 3- and 4-phonon scattering and are shown for a diverse range of materials (Si, HgTe, MgO, TiNiSn and monoclinic ZrO$_2$). In addition to accelerating state-of-the-art thermal transport calculations, the method shown here paves the way for modeling strongly anharmonic materials and higher-order phonon interactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05913 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tensor Learning and Compression of N-phonon Interactions Luo, Yao Mangtani, Dhruv Peng, Shiyu Yao, Jia Kliavinek, Sergei Bernardi, Marco Materials Science Computational Physics Phonon interactions from lattice anharmonicity govern thermal properties and heat transport in materials. These interactions are described by n-th order interatomic force constants (n-IFCs), which can be viewed as high-dimensional tensors correlating the motion of n atoms, or equivalently encoding n-phonon scattering processes in momentum space. Here, we introduce a tensor decomposition to efficiently compress n-IFCs for arbitrary order n. Using tensor learning, we find optimal low-rank approximations of n-IFCs by solving the resulting optimization problem. Our approach reveals the inherent low dimensionality of phonon-phonon interactions and allows compression of the 3 and 4-IFC tensors by factors of up to $10^3-10^4$ while retaining high accuracy in calculations of phonon scattering rates and thermal conductivity. Calculations of thermal conductivity using the compressed n-IFCs achieve a speed-up by nearly three orders of magnitude with >98% accuracy relative to the reference uncompressed solution. These calculations include both 3- and 4-phonon scattering and are shown for a diverse range of materials (Si, HgTe, MgO, TiNiSn and monoclinic ZrO$_2$). In addition to accelerating state-of-the-art thermal transport calculations, the method shown here paves the way for modeling strongly anharmonic materials and higher-order phonon interactions. |
| title | Tensor Learning and Compression of N-phonon Interactions |
| topic | Materials Science Computational Physics |
| url | https://arxiv.org/abs/2503.05913 |