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Machine learning predicts thermal conductivity in layered materials

Paper Authors:

Yong Lu,

Fawei Zheng

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Key Details

Combines machine learning and Boltzmann transport theory for thermal conductivity

Captures multi-order anharmonic phonon scattering processes

Enables large-scale, precise quantification of lattice heat conduction

Respects contribution of each phonon mode

Demonstrated for layered transition metal dichalcogenide ZrSe2

AI generated summary

Machine learning predicts thermal conductivity in layered materials

This paper develops a hybrid computational approach combining machine learning force fields and the Boltzmann transport equation to accurately predict lattice thermal conductivity in layered materials like transition metal dichalcogenides. The approach captures complex anharmonic phonon interactions at large scales to enable precise quantification of heat conduction.

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