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This work aims to improve the prediction of temperature-dependent phonons in inorganic materials.

July 2, 2026Small

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

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Randomized trial predicts temperature-dependent phonons in inorganic materials, suggesting new material discovery pathways.

This work aims to improve the prediction of temperature-dependent phonons in inorganic materials.
Combines machine learning interatomic potentials with anharmonic lattice dynamics.
High-throughput calculations performed on 4669 inorganic compounds to compute phonon data.
Refined the M3GNet potential using high-quality phonon data for increased accuracy.
Improved phonon prediction accuracy fourfold while maintaining computational efficiency.
Identified key drivers of strong anharmonicity: weak bonding, large atomic radii, and specific coordination motifs.
Anharmonic effects can alter lattice thermal conductivity by factors of two to four.

Lee et al. (2026) studied this question.

View Full Paper

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July 2, 2026Small

View Full Paper

Ask AI

Bookmark

Authors

Randomized trial predicts temperature-dependent phonons in inorganic materials, suggesting new material discovery pathways.

This work aims to improve the prediction of temperature-dependent phonons in inorganic materials.
Combines machine learning interatomic potentials with anharmonic lattice dynamics.
High-throughput calculations performed on 4669 inorganic compounds to compute phonon data.
Refined the M3GNet potential using high-quality phonon data for increased accuracy.
Improved phonon prediction accuracy fourfold while maintaining computational efficiency.
Identified key drivers of strong anharmonicity: weak bonding, large atomic radii, and specific coordination motifs.
Anharmonic effects can alter lattice thermal conductivity by factors of two to four.

Lee et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark