First-principles-based machine learning interatomic potential for molecular dynamics simulations of 2D lateral MoS2/WS2 heterostructures

Abstract

Understanding the mechanical and thermodynamic properties of transition-metal dichalcogenides (TMDs) and their heterostructures is pivotal for advancing the development of flexible semiconductor devices, and molecular dynamics (MD) simulation is widely applied to study these properties. However, current uncertainties persist regarding the efficacy of empirical potentials in MD simulations to accurately describe the intricate performance of complex interfaces within heterostructures. This study addresses these challenges by developing an interatomic potential based on deep neural networks and first-principles calculations. Specifically focusing on MoS2/WS2 heterostructures, our approach aims to predict Young's modulus and thermal conductivities. The potential's effectiveness is demonstrated through the validation of structural features, mechanical properties, and thermodynamic characteristics, revealing close alignment with values derived from first-principles calculations. A noteworthy finding is the substantial influence of the load direction on Young's modulus of heterostructures. Furthermore, our results highlight that the interfacial thermal conductance of the MoS2/WS2 heterostructures is considerably larger than that of graphene-based interfaces. The potential developed in this work facilitates large-scale material simulations, bridging the gap with first-principles calculations. Notably, it outperforms empirical potentials under interface conditions, establishing its significant competitiveness in simulation computations. Our approach not only contributes to a deeper understanding of TMDs and heterostructures but also presents a robust tool for the simulation of their mechanical and thermal behaviors, paving the way for advancements in flexible semiconductor device manufacturing.