Data-Driven Exploration and Insights Into Temperature-Dependent Phonons in Inorganic Materials.

Phonons, quantized vibrations of the atomic lattice, are central to thermal transport, structural stability, and phase behavior in crystalline solids. However, most large-scale materials databases rely on the harmonic approximation and neglect important temperature-dependent anharmonic effects. Here, we present a scalable framework combining machine learning interatomic potentials, anharmonic lattice dynamics, and high-throughput calculations to predict finite-temperature phonons across thousands of materials. By fine-tuning the universal M3GNet potential with high-quality phonon data, we improve phonon prediction accuracy fourfold while retaining computational efficiency. We integrate this refined model with a high-throughput implementation of the stochastic self-consistent harmonic approximation to compute temperature-dependent phonons for 4669 inorganic compounds. The resulting dataset reveals systematic elemental and structural trends in anharmonic phonon renormalization, especially in alkali metals, perovskite-derived frameworks, and related systems. Machine learning analysis identifies weak bonding, large atomic radii, and specific coordination motifs as key drivers of strong anharmonicity. First-principles validation further shows that anharmonic effects can change lattice thermal conductivity by factors of two to four. This work provides an efficient data-driven platform for predicting finite-temperature phonon behavior and guiding the discovery of materials with tailored thermal and vibrational properties.