2025-09-16 カリフォルニア工科大学（Caltech）
Inspired by recent advances in machine learning, Caltech scientists have developed an AI-based technique that sifts through the high-order tensors that encode phonon interactions in a material and extracts only the crucial bits needed to complete the calculations that explain thermal transport.Credit: Rosa Romano, EAS Communications/Caltech
＜関連情報＞
- https://www.caltech.edu/about/news/new-ai-technique-unravels-quantum-atomic-vibrations-in-materials
- https://journals.aps.org/prl/abstract/10.1103/nmgj-yq1g
Nフォノン相互作用のテンソル学習と圧縮 Tensor Learning and Compression of N-Phonon Interactions
Yao Luo, Dhruv Mangtani, Shiyu Peng, Jia Yao, Sergei Kliavinek, and Marco Bernardi
Physical Review Letters Published: 16 September, 2025
DOI: https://doi.org/10.1103/nmgj-yq1g
Abstract
Phonon interactions from lattice anharmonicity govern thermal properties and heat transport in materials. These interactions are described by th order interatomic force constants (IFCs), which can be viewed as high-dimensional tensors correlating the motion of atoms, or equivalently encoding -phonon scattering processes in momentum space. Here, we introduce a tensor decomposition to efficiently compress IFCs for arbitrary order . Using tensor learning, we find optimal low-rank approximations of IFCs by solving the resulting optimization problem. Our approach reveals the inherent low dimensionality of phonon-phonon interactions and allows compression of the three- and four-IFC tensors by factors of up to 103–104 while retaining high accuracy in calculations of phonon scattering rates and thermal conductivity. Calculations of thermal conductivity using the compressed IFCs achieve a speedup by nearly 3 orders of magnitude with >98% accuracy relative to the reference uncompressed solution. These calculations include both three- and four-phonon scattering and are shown for a diverse range of materials (Si, HgTe, MgO, TiNiSn, and ZrO2). 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.
