Accurate Modeling of Interfacial Thermal Transport in van der Waals Heterostructures via Hybrid Machine Learning and Registry-Dependent Potentials

Accurate Modeling of Interfacial Thermal Transport in van der Waals Heterostructures via Hybrid Machine Learning and Registry-Dependent Potentials | 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 Accurate Modeling of Interfacial Thermal Transport in van der Waals Heterostructures via Hybrid Machine Learning and Registry-Dependent Potentials Wenwu Jiang, HeKai Bu, Ting Liang, Penghua Ying, Zheyong Fan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6526920/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Two-dimensional transition metal dichalcogenides (TMDs) exhibit remarkable thermal anisotropy due to their strong intralayer covalent bonding and weak interlayer van der Waals (vdW) interactions. However, accurately modeling their thermal transport properties remains a significant challenge, primarily due to the computational limitations of density functional theory (DFT) and the inaccuracies of classical force fields in non-equilibrium regimes. To address this, we use a recently developed hybrid computational framework that combines machine learning potential (MLP) for intralayer interactions with registry-dependent interlayer potential (ILP) for anisotropic vdW interlayer interaction, achieving near quantum mechanical accuracy. This approach demonstrates exceptional agreement with DFT calculations and experimental data for TMD systems, accurately predicting key properties such as lattice constants, bulk modulus, moiré reconstruction, phonon spectra, and thermal conductivities. The scalability of this method enables accurate simulations of TMD heterostructures with large-scale moiré superlattices, making it a transformative tool for the design of TMD-based thermal metamaterials and devices, bridging the gap between accuracy and computational efficiency. Physical sciences/Physics/Condensed matter physics/Surfaces interfaces and thin films Physical sciences/Materials science/Theory and computation/Computational methods Physical sciences/Materials science/Nanoscale materials/Two dimensional materials machine-learned potentials neuroevolution potential anisotropic interlayer potential transition metal dichalcogenides interfacial thermal transport Full Text Additional Declarations No competing interests reported. Supplementary Files SupportingInformation.pdf Cite Share Download PDF Status: Posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6526920","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":452340953,"identity":"a9659051-9b6a-44d1-8d4d-fd80659864f3","order_by":0,"name":"Wenwu Jiang","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Wenwu","middleName":"","lastName":"Jiang","suffix":""},{"id":452340954,"identity":"02ce0a34-6fa0-4a53-ba1b-0742971b5a5d","order_by":1,"name":"HeKai Bu","email":"","orcid":"","institution":"Wuhan 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However, accurately modeling their thermal transport properties remains a significant challenge, primarily due to the computational limitations of density functional theory (DFT) and the inaccuracies of classical force fields in non-equilibrium regimes. To address this, we use a recently developed hybrid computational framework that combines machine learning potential (MLP) for intralayer interactions with registry-dependent interlayer potential (ILP) for anisotropic vdW interlayer interaction, achieving near quantum mechanical accuracy. This approach demonstrates exceptional agreement with DFT calculations and experimental data for TMD systems, accurately predicting key properties such as lattice constants, bulk modulus, moiré reconstruction, phonon spectra, and thermal conductivities. 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