Transferable Dispersion-Aware Machine Learning Interatomic Potentials for Multilayer Transition Metal Dichalcogenide Heterostructures

Transferable Dispersion-Aware Machine Learning Interatomic Potentials for Multilayer Transition Metal Dichalcogenide Heterostructures | 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 Transferable Dispersion-Aware Machine Learning Interatomic Potentials for Multilayer Transition Metal Dichalcogenide Heterostructures Yusuf Shaidu, Mit H. Naik, Steven G. Louie, Jeffrey B. Neaton This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6406568/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Aug, 2025 Read the published version in npj Computational Materials → Version 1 posted 11 You are reading this latest preprint version Abstract Stacking atomically thin layers of transition metal dichalcogenides (TMDs) to form heterostructures provides a powerful and versatile platform for investigating exotic quantum phases. Controlling the twist-angle between the TMDs creates moir'e superlattices that fundamentally alter their electronic and optical response. This has led to fascinating discoveries such as novel excitons, fractional quantum hall states, and unconventional magnetism. The emergence of many of these unique electronic phases can be attributed to substantial structural rearrangement of atoms within the moir'e pattern. Hence, understanding the structural reconstruction of TMD moir'e superlattices is the essential first step to understanding its unique electronic and optical properties. However, due to the large number of atoms in a moir'e unit-cell, studying this reconstruction using density functional theory (DFT) is computationally prohibitive. The spacing between atoms in TMD bilayers can be as large as 10 $\mathrm{\AA}$, making traditional neural network potentials (NNPs) inefficient to account for long-range van der Waals interactions. Here, we develop a new NNP architecture that is general, transferrable and includes long-range dispersion corrections that accounts for van der Waals interactions up to 12 $\mathrm{\AA}$ with minimal computational overhead. The NNP is fitted to van der Waals corrected DFT calculations for layered semiconducting TMDs containing transition metals Mo and W and chalcogens S, Se and Te. This NNP is accurate for monolayers, homobilayers and heterostructures as well as their interaction with commonly used hexagonal boron nitride substrates. The NNP shows excellent performance with respect to van der Waals-corrected DFT on equilibrium lattice parameters, potential energy surface and phonon dispersions. Furthermore, we accurately reproduce the experimentally measured reconstruction of twisted WS\text{$_2$} and MoS\text{$_2$}/WSe\text{$_2$} heterostructures and demonstrate the role played by the substrate in the measured corrugation amplitude. These results suggest that our NNP can be used to compute a wide range of properties of semiconducting TMDs with the accuracy of DFT while maintaining excellent computational efficiency. Physical sciences/Materials science/Nanoscale materials/Two dimensional materials Physical sciences/Materials science/Materials for energy and catalysis Physical sciences/Materials science/Theory and computation Full Text Additional Declarations No competing interests reported. Supplementary Files SI.pdf Cite Share Download PDF Status: Published Journal Publication published 24 Aug, 2025 Read the published version in npj Computational Materials → Version 1 posted Editorial decision: Revision requested 06 May, 2025 Reviews received at journal 28 Apr, 2025 Reviews received at journal 24 Apr, 2025 Reviews received at journal 19 Apr, 2025 Reviewers agreed at journal 14 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers invited by journal 09 Apr, 2025 Editor assigned by journal 09 Apr, 2025 Submission checks completed at journal 09 Apr, 2025 First submitted to journal 08 Apr, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6406568","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":449329796,"identity":"963bf348-5e8b-4f8a-81a2-e19daf1f5528","order_by":0,"name":"Yusuf Shaidu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYFACHgaGBDYGOQMGBmYGhgKwkAFRWowhWgyI1cLAxpC4gWgtBufPHvzwoOxw+nb23scGHwxsEhvYm7dJ4NVyIy9ZIuHc4dydPceNE2cYpCU28BwrI6CFx0Aise1w7oYbacyHeQwOJzZI5Jjh13L+jPEPoJZ0g/vPmA//Mfif2CD/hoCWA0AzgVoSDG6wMScDuUBbePBrkbyRY2aRcC7dcGdPGrNhj0GycRtPWrEFPi18QIfd/FFmLW/OfoxZ4keFnWw/++GNN/BpUTiAJuDYhk85CMg3oAnYE9IxCkbBKBgFIw8AACuETA4m0J9FAAAAAElFTkSuQmCC","orcid":"","institution":"University of California, Berkeley","correspondingAuthor":true,"prefix":"","firstName":"Yusuf","middleName":"","lastName":"Shaidu","suffix":""},{"id":449329797,"identity":"df7a2b86-a180-48a9-9df1-7c718de6c327","order_by":1,"name":"Mit H. 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Controlling the twist-angle between the TMDs creates moir\\'e superlattices that fundamentally alter their electronic and optical response. This has led to fascinating discoveries such as novel excitons, fractional quantum hall states, and unconventional magnetism. The emergence of many of these unique electronic phases can be attributed to substantial structural rearrangement of atoms within the moir\\'e pattern. Hence, understanding the structural reconstruction of TMD moir\\'e superlattices is the essential first step to understanding its unique electronic and optical properties. However, due to the large number of atoms in a moir\\'e unit-cell, studying this reconstruction using density functional theory (DFT) is computationally prohibitive. The spacing between atoms in TMD bilayers can be as large as 10 $\\mathrm{\\AA}$, making traditional neural network potentials (NNPs) inefficient to account for long-range van der Waals interactions. Here, we develop a new NNP architecture that is general, transferrable and includes long-range dispersion corrections that accounts for van der Waals interactions up to 12 $\\mathrm{\\AA}$ with minimal computational overhead. The NNP is fitted to van der Waals corrected DFT calculations for layered semiconducting TMDs containing transition metals Mo and W and chalcogens S, Se and Te. This NNP is accurate for monolayers, homobilayers and heterostructures as well as their interaction with commonly used hexagonal boron nitride substrates. The NNP shows excellent performance with respect to van der Waals-corrected DFT on equilibrium lattice parameters, potential energy surface and phonon dispersions. Furthermore, we accurately reproduce the experimentally measured reconstruction of twisted WS\\text{$_2$} and MoS\\text{$_2$}/WSe\\text{$_2$} heterostructures and demonstrate the role played by the substrate in the measured corrugation amplitude. These results suggest that our NNP can be used to compute a wide range of properties of semiconducting TMDs with the accuracy of DFT while maintaining excellent computational efficiency. 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