Probing the ideal limit of interfacial thermal conductance in two-dimensional van der Waals heterostructures
arXiv:2502.13601 · doi:10.1038/s41524-025-01885-y
Abstract
Probing the ideal limit of interfacial thermal conductance (ITC) in two-dimensional (2D) heterointerfaces is of paramount importance for assessing heat dissipation in 2D-based nanoelectronics. Using graphene/hexagonal boron nitride (Gr/-BN), a structurally isomorphous heterostructure with minimal mass contrast, as a prototype, we develop an accurate yet highly efficient machine-learned potential (MLP) model, which drives nonequilibrium molecular dynamics (NEMD) simulations on a realistically large system with over 300,000 atoms, enabling us to report the ideal limit range of ITC for 2D heterostructures at room temperature. We further unveil an intriguing stacking-sequence-dependent ITC hierarchy in the Gr/-BN heterostructure, which can be connected to moiré patterns and is likely universal in van der Waals layered materials. The underlying atomic-level mechanisms can be succinctly summarized as energy-favorable stacking sequences facilitating out-of-plane phonon energy transmission. This work demonstrates that MLP-driven MD simulations can serve as a new paradigm for probing and understanding thermal transport mechanisms in 2D heterostructures and other layered materials.
13 pages, 6 figures in the main text; 20 figures in the SI
References in corpus (17)
- Emergence of Superlattice Dirac Points in Graphene on Hexagonal Boron Nitride
- Van der Waals heterostructures for high-performance device applications: challenges and opportunities
- The phonon dispersion of graphite by inelastic x-ray scattering
- GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
- Molecular dynamics simulations of heat transport using machine-learned potentials: A mini review and tutorial on GPUMD with neuroevolution potentials
- Controllable Thermal Conductivity in Twisted Homogeneous Interfaces of Graphene and Hexagonal Boron Nitride
- Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine-learning molecular dynamics simulations
- Mechanical and Tribological Properties of Layered Materials Under High Pressure: Assessing the Importance of Many-Body Dispersion Effects
- Sub-micrometer phonon mean free paths in metal-organic frameworks revealed by machine-learning molecular dynamics simulations
- Accurate prediction of heat conductivity of water by a neuroevolution potential
- Thermal conduction across a boron nitride and silicon oxide interface
- Mechanisms of temperature-dependent thermal transport in amorphous silica from machine-learning molecular dynamics
- Tuning the lattice thermal conductivity in van-der-Waals structures through rotational (dis)ordering
- Engineering thermal transport across layered graphene-MoS2 superlattices
- Parity-Dependent Moiré Superlattices in Graphene/h-BN Heterostructures: A Route to Mechanomutable Metamaterials
- Moiré-Driven Interfacial Thermal Transport in Twisted Transition Metal Dichalcogenides
- PYSED: A tool for extracting kinetic-energy-weighted phonon dispersion and lifetime from molecular dynamics simulations