Impact of classical statistics on thermal conductivity predictions of BAs and diamond using machine learning molecular dynamics
At a Glance
Section titled āAt a Glanceā| Metadata | Details |
|---|---|
| Publication Date | 2024-10-21 |
| Journal | Applied Physics Letters |
| Authors | Hao Zhou, Shuxiang Zhou, Zilong Hua, Kaustubh Bawane, Tianli Feng |
| Institutions | University of Utah, Idaho National Laboratory |
| Citations | 3 |
Abstract
Section titled āAbstractāMachine learning interatomic potentials (MLIPs) have greatly enhanced molecular dynamics (MD) simulations, achieving near-first-principles accuracy in thermal conductivity studies. In this work, we reveal that this accuracy, observed in BAs and diamond at sub-Debye temperatures, stems from an accidental error cancelation: classical statistics overestimates specific heat while underestimating phonon lifetimes, balancing out in thermal conductivity predictions. However, this balance is disrupted when isotopes are introduced, leading MLIP-based MD to significantly underpredict thermal conductivity compared to experiments and quantum statistics-based Boltzmann transport equation. This discrepancy arises not from classical statistics affecting phonon-isotope scattering rates but from its impact on the interplay between phonon-isotope and phonon-phonon scattering in the normal scattering-dominated BAs and diamond. This work underscores the limitations of MLIP-based MD for thermal conductivity studies at sub-Debye temperatures.
Tech Support
Section titled āTech SupportāOriginal Source
Section titled āOriginal SourceāReferences
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