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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.04939 |
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| _version_ | 1866910032022470656 |
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| author | Zhao, Luneng Liu, Hongsheng Chang, Yuan Shi, Xiaoran Zhao, Jijun Ding, Feng Gao, Junfeng |
| author_facet | Zhao, Luneng Liu, Hongsheng Chang, Yuan Shi, Xiaoran Zhao, Jijun Ding, Feng Gao, Junfeng |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04939 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Intermediates of Forming Transition Metal Dichalcogenide Heterostructures Revealed by Machine Learning Simulations Zhao, Luneng Liu, Hongsheng Chang, Yuan Shi, Xiaoran Zhao, Jijun Ding, Feng Gao, Junfeng Materials Science 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). |
| title | Intermediates of Forming Transition Metal Dichalcogenide Heterostructures Revealed by Machine Learning Simulations |
| topic | Materials Science |
| url | https://arxiv.org/abs/2405.04939 |