Abstract
Abstract Reducing thermal boundary resistance (TBR) is critical for enhancing thermal management efficiency and optimizing the performance of electronic and thermoelectric devices. In this study, we employed non-equilibrium molecular dynamics (NEMD) simulations using Neuroevolution Potential (NEP) machine learning models to investigate how embedding nanoparticles in Si/Ge heterostructures impacts TBR. Our results show a significant reduction in TBR, attributed to enhanced phonon density of states matching via resonance, promoting more efficient elastic phonon transport across the interface. However, this approach also leads to a substantial increase in bulk thermal resistance, highlighting a trade-off where overall heat dissipation is compromised. To address this, we propose an alternative strategy of positioning a nanoparticle directly at the interface to modulate interfacial modes, improving phonon transport efficiency without adversely affecting bulk thermal properties. NEMD simulations validate this approach, showing comparable TBR reduction while mitigating the bulk thermal resistance increase observed with the resonance-based embedding method. This study offers valuable insights into resolving interfacial heat dissipation challenges, providing a balanced strategy for optimizing thermal transport efficiency in nanoscale material systems.
Published Version
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