Probing the ideal limit of interfacial thermal conductance in two-dimensional van der Waals heterostructures

TL;DR

This study uses machine learning-enhanced molecular dynamics to determine the maximum interfacial thermal conductance in 2D heterostructures, revealing stacking-dependent effects and atomic mechanisms influencing heat transfer.

Contribution

Develops an accurate machine-learned potential enabling large-scale MD simulations to explore the ideal interfacial thermal conductance in 2D heterostructures, highlighting stacking effects.

Findings

Identifies the ideal ITC range for 2D heterostructures at room temperature.

Discovers stacking-sequence-dependent ITC hierarchy linked to moiré patterns.

Shows atomic stacking sequences influence phonon energy transmission.

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/$h$-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/$h$-BN heterostructure, which can be connected to moir\'e 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.