Monoclinic gallium oxide (β-Ga₂O₃) is a fourth-generation semiconductor with great application potential in high-power microelectronics. Recent studies indicate that the electrical conductivity of β-Ga₂O₃ can be substantially enhanced through silicon (Si) doping. However, the effects on thermal transport, especially by considering the practical nanostructures within the crystal, have not yet been explored. To address this gap, we have developed a unified neural network potential for investigating the unexplored phonon transport of the β-(Si_xGa_1–x)₂O₃ with varying doping levels. Our atomistic simulations showed that compared to intrinsic β-Ga₂O₃, the room-temperature thermal conductivities respectively decreased by 36.5%, 33.5%, and 38.8% along the a, b, and c axes in β-SiGa₅₁₁O₇₆₈, and by 79.6%, 74.9%, and 77.8% in β-SiGa₇O₁₂. The significant degradation in phonon transport is attributed to increased lattice anharmonicity, reduced sound velocity, and most importantly, induced phonon localization due to Si substitutions. A quantitative analysis reveals that the localization primarily occurs in phonons with frequencies exceeding 2.5 THz. The vibration is confined to a region around the Si atom, extending only to its second-nearest neighbors.

With the development of artificial intelligence technology, machine learning atomic interaction potential has become popular to solve a problem regarding the low accuracy of empirical potential. Machine learning atomic interaction potential avoids a low efficiency of conventional fitting method for empirical potential and becomes an emerging tool for material exploration and research. This review represented the characteristics of existing machine learning potential and the applications in phase change, intrinsic properties and interface researches. In addition, the challenge and development trends of machine learning atomic interaction potential were also prospected.

Polymer composites as thermal interface materials have been widely used in modern electronic equipment. In this work, we report a novel method to prepare highly through-plane thermally conductive silicone rubber (SR) composites with vertically aligned silicon carbide fibers (VA-SiCFs) entangled by SiC nanowires (SiCNWs) networks. First, a series of carbon fibers (CFs) skeletons were fabricated in sequence of coating poor thermally conductive polyacrylonitrile-based CFs with polydopamine, ice-templated assembly, and freeze-drying processes. Furthermore, VA-SiCFs networks, i.e., long-range continuous SiCFs-SiCNWs networks, based on the prepared CFs skeletons, were in-situ obtained via template-assisted chemical vapor deposition method. The thermal conductivity enhancement mechanism of VA-SiCFs networks on its SR composites was also intensively studied by finite element simulation, based on the first principles investigation of SiC, and Foygel’s theory. The in-situ grown VA-SiCFs networks possess high intrinsic thermal conductivity without the thermal interface between fillers, acting as the high-efficiency through-plane long-range continuous thermal conduction path, in which the SiCNWs were the in-plane “thermal spreader”. The VA-SiCFs/SR composites reached a high through-plane thermal conductivity, 2.13 W/(m·K), at the filler loading of 15 vol.%, which is 868.2%, and 249.2% higher than that of pure SR sample, and random-CFs@polydopamine (PDA)/SR composites at the same content, respectively. The VA-SiCFs/SR composites also exhibited good electrical insulation performance and excellent dimensional stability, which guaranteed the stable interfacial heat transfer of high-power density electronic devices.