Machine-learning-derived thermal conductivity of two-dimensional TiS2/MoS2 van der Waals heterostructures - A. K. Nair; C. M. Da Silva; C. H. Amon (APL Machine Learning, 2024)
Predicting the thermal conductivity of two-dimensional (2D) heterostructures is challenging and cannot be adequately resolved using conventional computational approaches. To address this challenge, we propose a new and efficient approach that combines first-principles density functional theory (DFT) calculations with a machine-learning interatomic potential (MLIP) methodology to determine the thermal conductivity of a novel 2D van der Waals TiS2/MoS2 heterostructure. We leverage the proposed approach to estimate the thermal conductivities of TiS2/MoS2 heterostructures as well as bilayer-TiS2 and bilayer-MoS2. A unique aspect of this approach is the combined implementation of the moment tensor potential for short-range (intralayer) interactions and the D3-dispersion correction scheme for long-range (interlayer) van der Waals interactions. This approach employs relatively inexpensive computational DFT-based datasets generated from ab initio molecular dynamics simulations to accurately describe the interatomic interactions in the bilayers. The thermal conductivities of the bilayers exhibit the following trend: bilayer-TiS2 > bilayer-MoS2 > the TiS2/MoS2 heterostructure. In addition, this work makes the case that the 2D bilayers exhibit considerably higher thermal conductivities than bulk graphite, a common battery anode material, indicating the potential to utilize 2D heterostructures in thermal management applications and energy storage devices. Furthermore, the MLIP-based methodology provides a reliable approach for estimating the thermal conductivity of bilayers and heterostructures.