[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127360-en":3,"doc-seo-127360-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127360,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Intermediates of Forming Transition Metal Dichalcogenides Heterostructures Revealed by Machine Learning Simulations","Machine learning simulations are used to study the controlled growth of 2D transition metal dichalcogenide (TMD) vdW heterostructures, focusing on bilayer MoS2/WS2. A trained machine-learning potential implemented in molecular dynamics enables accurate large-scale modeling of complex bonding rearrangements during deposition. The work identifies SMMS as a highly stable intermediate that promotes metal atom exchange and alloying, explaining contamination mechanisms. It also shows that suppressing bare metal adsorption avoids SMMS to enable pristine heterostructures, while SMMS can act as an ideal low-Schottky-barrier electrode for MoS2 FETs.","arXiv :2405 .04939v2 [ cond-mat .mtrl-sci ] 8 Jul 2024  \nIntermediates of Forming Transition Metal Dichalcogenides Heterostructures Revealed by Machine Learning Simulations  \nLuneng Zhao, 1 Hongsheng Liu, 1 Yuan Chang, 1 Xiaoran Shi, 1 Junfeng Gao, 1, ∗ Jijun Zhao, 1 and Feng Ding2,†  \n1 State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment & School of Physics, Dalian University of Technology, Dalian 116024, People’s Republic of China  \n2 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Dated: July 9, 2024)  \nThe primary restrictions on 2D transition metal dichalcogenides (TMD) vdW 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 letter, we developed a machine learning potential (MLP) which can accurately simulate the growth processes of bilayer MoS2 /WS2 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 MoS2 FETs with low Schottky barrier.  \nTwo-dimensional (2D) TMDs have attracted intense attention due to their suitable band gap and fast carriers [1–4], high nonlinear optical response [5, 6], ease of layer assembly, etc. vdWHs integrated by pristine TMDs can vastly tune their properties and lead to immense potential applications in microelectronics, optoelectronics, and nonlinear optics [7–14] .  \nHowever, the controlled growth of TMD vdWHs still faces many challenges. Commonly used mechanical assembly methods can achieve high-quality TMD vdWHs with pristine structures [15, 16], but it is hard to reach wafer-size and is too expensive. Chemical vapor deposition (CVD) has achieved great success in growing wafersize monolayer TMD [12, 17 , 18] . Yet, growing TMDvdWHs still faces limitations in wafer size and a tendency to alloy [19] . Among these methods, a feasible approach may be metal-organic chemical vapor deposition (MOCVD) [20] .  \nRecently, a two-step vapor deposition process with a high-to-low temperature strategy was reported to synthesize wafer-size TMD vdWHs [21] . This method first grows a monolayer WS2 film by depositing a W film on a sapphire substrate and sulfurizing it at 900°C (the highest temperature among the four stacked materials) . Next, a Mo film is deposited on the WS2 monolayer via magnetron sputtering and selenized at 800°C to form MoS2 . Subsequently, an Nb film is deposited on the WS2 /MoS2 film via magnetron sputtering and selenizedat 700°C to form NbSe2 . Finally, PtTe2 is grown on the WS2 /MoS2 /NbSe2 vdWHs at 350°C (the lowest temperature in the sequence) . The final structure is a wafer-size vdWHs (several centimeters) consisting of four layers: WS2 /MoS2 /NbSe2 /PtTe2 . The authors propose that during the metal deposition process, the metal atom films float on the TMD surface.  \nCompared to experimental trials with many parameters and various growth conditions, accurate atomic simulations can certainly provide more insightful under-  \nstanding of the growth of TMD vdWHs. The growth of TMD vdWHs is always accompanied by rapid formation and breaking, and includes various intermediate motifs with complex chemical bonds, such as metal bonds, covalent bonds, both covalent and ionic characters of Mo-S bonds, and layered vdW forces. Density functional theory (DFT) can describe the complex chemical bonding but cannot afford large-scale simulations. In contrast, classical molecular dynamics (MD) can simulate large systems but cannot handle complex chemical bond recombination. Although various MLP have been developed and reported to simulate large systems with ac","cbCaigbGX5YD2m63","https://ap.wps.com/l/cbCaigbGX5YD2m63","pdf",9509032,1,6,"English","en",105,"# Introduction\n## Growth challenges and existing methods\n# Machine-learning potential and simulation approach\n## Model development and integration with MD\n# Key findings and implications\n## SMMS intermediate: stability, alloying, and electrode behavior\n# Data and training considerations\n## Coverage, configurations, and balanced weighting","[{\"question\":\"What limits the controlled growth of 2D TMD vdW heterostructures?\",\"answer\":\"The main restrictions highlighted are size limitation and alloying during growth.\"},{\"question\":\"How does the developed machine-learning potential improve simulation of heterostructure growth?\",\"answer\":\"It is trained on extensive DFT data using a revised equivariant graph neural network and embedded into molecular dynamics to achieve both large-scale and DFT-level accuracy for complex growth processes.\"},{\"question\":\"What is the role of the SMMS structure in TMD heterostructure formation?\",\"answer\":\"SMMS is revealed as a highly stable intermediate that enables metal atom exchange and drives alloying; avoiding SMMS by preventing bare metal adsorption helps produce pristine heterostructures.\"}]","Intermediates of Forming Transition Metal Dichalcogenides Heterostructures Revealed by Machine Learning Simulations | PDF",1785938495,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"intermediates-of-forming-transition-metal-dichalcogenides-heterostructures-revealed-by-machine-learning-simulations","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/intermediates-of-forming-transition-metal-dichalcogenides-heterostructures-revealed-by-machine-learning-simulations/127360/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What limits the controlled growth of 2D TMD vdW heterostructures?","Question",{"text":75,"@type":76},"The main restrictions highlighted are size limitation and alloying during growth.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the developed machine-learning potential improve simulation of heterostructure growth?",{"text":80,"@type":76},"It is trained on extensive DFT data using a revised equivariant graph neural network and embedded into molecular dynamics to achieve both large-scale and DFT-level accuracy for complex growth processes.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the role of the SMMS structure in TMD heterostructure formation?",{"text":84,"@type":76},"SMMS is revealed as a highly stable intermediate that enables metal atom exchange and drives alloying; avoiding SMMS by preventing bare metal adsorption helps produce pristine heterostructures.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]