【Others Papers】Unveiling thermal transport properties of defective β−Ga₂O₃ through machine learning potentials

      Researchers from the Jilin University have published a dissertation titled "Unveiling thermal transport properties of defective β−Ga₂O₃ through machine learning potentials" in Physical Review Materials.

Abstract

      The intrinsically low thermal conductivity of β−Ga₂O₃ poses a major challenge for high-power and high-frequency electronic applications. This issue becomes more severe in the presence of defects, which further suppress heat dissipation, exacerbate self-heating, and degrade device performance. In this work, we develop an efficient machine learning potential (MLP) based on a deep neural network model for accurately describing pristine and point-defective β−Ga₂O₃. Using equilibrium molecular dynamics (EMD) simulations, we quantify the reduction in thermal conductivity induced by intrinsic point defects. Our results demonstrate that Ga interstitials exert the strongest suppression, decreasing the thermal conductivity by 79.5%. Moreover, Ga-related defects generally have a more pronounced impact than O-related defects. This behavior originates from the enhanced vibrations of weakly bonded Ga atoms, increased phonon anharmonicity, and a substantial reduction in the group velocity of low-frequency phonons. These results provide atomic-level insight into thermal transport in defective β−Ga₂O₃, offering guidance for thermal-management strategies and establishing a general workflow for investigating thermal physics in complex semiconductor materials.

DOI：

https://doi.org/10.1103/fdrt-2chf