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.