Caltech Researchers Develop AI for Faster Phonon Dynamics

In a breakthrough published in Physical Review Letters, Caltech researchers (Bernardi, Luo, et al.) unveil an AI-driven approach that dramatically compresses the complexity of high-order phonon interaction tensors. Using tensor decomposition techniques adapted for symmetry, their method delivers the same accuracy in thermal transport and phonon dynamics predictions 1,000-10,000× faster than traditional supercomputer methods. This advance opens up high-throughput screening of materials and deeper quantum insight into how atomic vibrations control material behavior including heat flow, phase changes, and thermal expansion. Read more: https://lnkd.in/gdykGtFz

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