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

Intermediates of Forming Transition Metal Dichalcogenides Heterostructures Revealed by Machine Learning Simulations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Intermediates of Forming Transition Metal Dichalcogenides Heterostructures Revealed by Machine Learning Simulations Junfeng Gao, Luneng Zhao, Hongsheng Liu, Yuan Chang, Xiaoran Shi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5424715/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Feb, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract The primary restrictions on 2D transition metal dichalcogenide (TMD) van der Waals heterostructures (vdWHs) are size limitation and alloying. Recently, a two-step vapor deposition method was reported to grow wafer-scale TMD vdWHs with little contamination [Nature 621, 499 (2023)]. In this study, we developed a machine learning potential (MLP) which can accurately simulate the growth processes of bilayer MoS 2 /WS 2 vdWHs under various conditions. Importantly, a SMMS (where M is Mo or W) structure is revealed as a highly stable intermediate easily introduces metal atom exchange and alloying. Eliminating the alloying contamination in TMD vdWHs is avoiding SMMS structure by preventing the landing of bare metal atoms. However, SMMS is revealed as an ideal electrode for MoS 2 FETs with low Schottky barrier. Physical sciences/Physics/Condensed-matter physics/Surfaces, interfaces and thin films Physical sciences/Nanoscience and technology/Nanoscale materials/Two-dimensional materials Physical sciences/Materials science/Theory and computation/Atomistic models Full Text Additional Declarations There is NO Competing Interest. Supplementary Files S4manysonsmomos.mp4 MLPMD Simulation of S Atom-Induced Mo Extraction and Bilayer MoS 2 Formation on SMoMoS Structure S5manysonsmms.mp4 MLPMD Simulation of S Atom-Induced Mo/W Extraction and Bilayer Alloyed Mo x W 1-x S 2 Formation from SMMS Structure S7mos2growthonws2.mp4 MLPMD Simulation of Mo and S Deposition on WS 2 Surface Forming Bilayer Non-Alloyed MoS 2 /WS 2 vdWHs S6mos2growthonmos2.mp4 MLPMD Simulation of Mo and S Atom Deposition Leading to Bilayer MoS 2 Formation supplementarymaterial.docx Supplementary Information S1mowsanneal.mp4 MLPMD Simulation of Mo-W-S Mixture Annealing and Mo x W 1-x S 2 Alloy Formation S2modepositeonmos2.mp4 MLPMD Simulation of Sequential Mo Atom Deposition and Embedding on MoS 2 Surface S3modepositeonws2.mp4 MLPMD Simulation of Sequential Mo Atom Deposition and Embedding on WS 2 Surface MachineLearningReportingSummary.pdf Machine Learning Reporting Summary Cite Share Download PDF Status: Published Journal Publication published 23 Feb, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5424715","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":386298976,"identity":"e399f0c2-3ef2-4900-84aa-2181219fee1d","order_by":0,"name":"Junfeng 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