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

Ting Liang, Ke Xu+10 more|ArXiv.org|Feb 19, 2025

Thermal properties of materials3 citations

TL;DR

The paper develops a machine-learned potential to perform large-scale NEMD simulations, revealing the ideal interfacial thermal conductance (ITC) range for 2D Gr/ h-BN heterostructures and a stacking-sequence dependent ITC hierarchy linked to moiré patterns.

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 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.

Motivation & Objective

• Motivate the need to understand heat dissipation in 2D nanoelectronics at 2D heterointerfaces.
• Establish an accurate, efficient computational framework to quantify the ideal ITC in 2D van der Waals interfaces.
• Use Gr/h-BN as a structurally isomorphous prototype with minimal mass contrast.
• Uncover how stacking sequence affects ITC and relate it to moiré patterns.

Proposed method

• Develop and train a machine-learned potential (MLP) suitable for 2D vdW heterostructures.
• Perform nonequilibrium molecular dynamics (NEMD) simulations on systems with over 300,000 atoms.
• Compute the ideal ITC range for 2D heterostructures at room temperature.
• Investigate stacking-sequence dependent ITC and relate findings to moiré-pattern effects.
• Interpret atomic-level mechanisms as energy-favorable stacking sequences facilitating out-of-plane phonon transmission.

Experimental results

Research questions

• RQ1 What is the ideal range of interfacial thermal conductance for graphene/ h-BN 2D heterostructures at room temperature?
• RQ2 How does stacking order affect ITC in Gr/h-BN, and can moiré patterns explain any hierarchy observed?
• RQ3 What atomic-level mechanisms govern energy transmission across Gr/h-BN interfaces?
• RQ4 Can machine-learned potentials enable accurate, large-scale MD to study thermal transport in 2D heterostructures?

Key findings

• An accurate, efficient MLP enables NEMD simulations on systems with over 300k atoms to determine ITC limits.
• A stacking-sequence dependent ITC hierarchy emerges in Gr/h-BN, likely connected to moiré patterns.
• Energy-favorable stacking sequences promote out-of-plane phonon energy transmission, enhancing ITC.
• MLP-driven MD is a viable paradigm for uncovering thermal transport mechanisms in 2D heterostructures.