Intermediates of Forming Transition Metal Dichalcogenide Heterostructures Revealed by Machine Learning Simulations

Abstract

Two-dimensional (2D) transition metal dichalcogenide (TMD) van der Waals heterostructures (vdWHs) hold promise for high-performance electronics, but their large-scale synthesis remains limited by size constraints and alloying contaminations. Recently, a two-step vapor deposition method was reported for growing wafer-size TMD vdWHs with minimal impurities. In this study, we develop a machine learning potential (MLP) that accurately captures the atomic-scale dynamic growth process of bilayer MoS${}_2$/WS${}_2$ vdWHs under feasible growth conditions. Our simulations uncover a crucial metastable SMMS (M = Mo or W) intermediate structure that facilitates metal atom swap and alloying. Eliminating the alloying contamination requires preventing the embedding of bare metal atoms. The results also show that the SMMS structure exhibits favourable electronic properties and emerges as a low Schottky barrier contact electrode for MoS${}_2$ field-effect transistors (FETs).

References in corpus (4)

• Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on-the-fly with Bayesian inference
• Towards compact phase-matched and waveguided nonlinear optics in atomically layered semiconductors
• Ultra-clean assembly of van der Waals heterostructures
• Size-Dependent Nucleation in Crystal Phase Transition from Machine Learning Metadynamics

Cited by in corpus (1)

• Machine Learning Interatomic Potentials Enable Molecular Dynamics Simulations of Doped MoS2