Intermediates of forming transition metal dichalcogenide heterostructures revealed by machine learning simulations

作者：Luneng Zhao, Hongsheng Liu, Yuan Chang, Xiaoran Shi, Jijun Zhao, Feng Ding, Junfeng Gao · 发表于：Nature Communications · 年份：2026 · DOI：10.1038/s41467-026-69977-x · 被引用次数：2 · 研究领域：Machine Learning in Materials Science、2D Materials and Applications

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 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 favorable electronic properties and emerges as a low Schottky barrier contact electrode for MoS 2 field-effect transistors (FETs).