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

TL;DR

This paper uses machine learning simulations to reveal intermediate structures in the growth of 2D transition metal dichalcogenide heterostructures, identifying a metastable phase that influences alloying and electronic properties.

Contribution

It introduces a machine learning potential to simulate atomic-scale growth, discovering a key metastable intermediate that impacts alloying and device performance.

Findings

Identified a metastable SMMS intermediate structure during growth.

Preventing metal atom embedding reduces alloying contamination.

SMMS structure offers favorable electronic properties for FET contacts.

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).