Accurate Modeling of Interfacial Thermal Transport in van der Waals Heterostructures via Hybrid Machine Learning and Registry-Dependent Potentials

Abstract

Two-dimensional transition metal dichalcogenides (TMDs) exhibit remarkable thermal anisotropy due to their strong intralayer covalent bonding and weak interlayer van der Waals (vdW) interactions. However, accurately modeling their thermal transport properties remains a significant challenge, primarily due to the computational limitations of density functional theory (DFT) and the inaccuracies of classical force fields in non-equilibrium regimes. To address this, we use a recently developed hybrid computational framework that combines machine learning potential (MLP) for intralayer interactions with registry-dependent interlayer potential (ILP) for anisotropic vdW interlayer interaction, achieving near quantum mechanical accuracy. This approach demonstrates exceptional agreement with DFT calculations and experimental data for TMD systems, accurately predicting key properties such as lattice constants, bulk modulus, moir\'e reconstruction, phonon spectra, and thermal conductivities. The scalability of this method enables accurate simulations of TMD heterostructures with large-scale moir\'e superlattices, making it a transformative tool for the design of TMD-based thermal metamaterials and devices, bridging the gap between accuracy and computational efficiency.