Dissimilar thermal transport properties in -GaO and -GaO revealed by machine-learning homogeneous nonequilibrium molecular dynamics simulations
arXiv:2311.01099 · doi:10.1063/5.0185854
Abstract
The lattice thermal conductivity (LTC) of GaO is an important property due to the challenge in the thermal management of high-power devices. We develop machine-learned neuroevolution potentials for single-crystalline -GaO and -GaO, and apply them to perform homogeneous nonequilibrium molecular dynamics simulations to predict their LTCs. The LTC of -GaO was determined to be 10.3 0.2 W/(m K), 19.9 0.2 W/(m K), and 12.6 0.2 W/(m K) along [100], [010], and [001], respectively, aligning with previous experimental measurements. For the first time, we predict the LTC of -GaO along [100], [010], and [001] to be 4.5 0.0 W/(m K), 3.9 0.0 W/(m K), and 4.0 0.1 W/(m K), respectively, showing a nearly isotropic thermal transport property. The reduced LTC of -GaO versus -GaO stems from its restricted low-frequency phonons up to 5 THz. Furthermore, we find that the phase exhibits a typical temperature dependence slightly stronger than , whereas the phase shows a weaker temperature dependence, ranging from to .
8 pages, 7 figures
References in corpus (11)
- Anisotropic Thermal Conductivity in Single Crystal beta-Gallium Oxide
- GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
- Improving the accuracy of the neuroevolution machine learning potential for multi-component systems
- Materials discovery and properties prediction in thermal transport via materials informatics: a mini-review
- Spectral Decomposition of Thermal Conductivity: Comparing Velocity Decomposition Methods in Homogeneous Molecular Dynamics Simulations
- Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine-learning molecular dynamics simulations
- Complex Polymorphs Explored by Accurate and General-Purpose Machine-Learning Interatomic Potentials
- Sub-micrometer phonon mean free paths in metal-organic frameworks revealed by machine-learning molecular dynamics simulations
- Mechanisms of temperature-dependent thermal transport in amorphous silica from machine-learning molecular dynamics
- Maximization and Minimization of Interfacial Thermal Conductance by Modulating the Mass Distribution of Interlayer
- Variable thermal transport in black, blue, and violet phosphorene from extensive atomistic simulations with a neuroevolution potential
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