Speaker
Description
Group-VI transition metal dichalcogenides, such as MoS$_2$ and MoSe$_2$, are prototypical two-dimensional materials with distinctive phononic and electronic properties, making them highly attractive for nanoelectronic, optoelectronic, and thermoelectric applications. However, their reported lattice thermal conductivities remain highly inconsistent, with experimental measurements and theoretical predictions differing by more than an order of magnitude.
In this work, we systematically investigate the origins of these discrepancies by combining first-principles calculations, molecular dynamics simulations, and machine-learning interatomic potentials (MLIPs). We employ and benchmark several state-of-the-art models, including GAP, MACE, NEP, and HIPHIVE, against density functional theory to assess their accuracy and transferability [1]. The computational efficiency of MLIPs enables extensive convergence studies that go beyond conventional limits, allowing a rigorous evaluation of higher-order anharmonic effects. In particular, we quantify the role of third- and fourth-order phonon scattering processes and validate our findings using homogeneous nonequilibrium molecular dynamics simulations.
Our results demonstrate that, contrary to recent claims, fully converged four-phonon scattering contributes negligibly to the intrinsic thermal conductivity of both MoS2 and MoSe2. This resolves a key source of inconsistency in the literature and establishes reliable benchmarks for these systems. More broadly, this work highlights the potential of machine-learning interatomic potentials as a robust and scalable framework for predictive modeling of phonon-mediated thermal transport in low-dimensional materials.
Reference:
[1] Tuğbey Kocabaş, Murat Keçeli, Tanju Gürel, Milorad V Milošević, Cem Sevik, Thermal conductivity limits of MoS$_2$ and MoSe$_2$: Revisiting high-order anharmonic lattice dynamics with machine learning potentials, Appl. Phys. Rev. 12, 041424 (2025).