A fine-tuned M3GNet machine-learning potential was used to compute temperature-dependent phonons for 4,669 inorganic compounds, showing alkali metals and perovskite-like frameworks have the strongest anharmonic renormalization.
Xia, Revisiting lattice thermal transport in pbte: The crucial role of quartic anharmonic- ity
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Data-Driven Exploration and Insights into Temperature-Dependent Phonons in Inorganic Materials
A fine-tuned M3GNet machine-learning potential was used to compute temperature-dependent phonons for 4,669 inorganic compounds, showing alkali metals and perovskite-like frameworks have the strongest anharmonic renormalization.