Physics-aware graph neural networks for automated tight-binding model construction in quantum transport simulations

Physics-aware graph neural networks for automated tight-binding model construction in quantum transport 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 Physics-aware graph neural networks for automated tight-binding model construction in quantum transport simulations Lei Liao, Yawei Lv, Shihong Yu, Weimin Tang, Haipeng Lan, Hui-Xiong Deng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7831376/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Tight-binding (TB) model is crucial for quantum transport simulations of semiconductor devices, critically determining the electrical characteristics of channel materials. Here, we propose a graph neural network (GNN)-based framework for automated TB model construction. By integrating atomic sites (nodes) and chemical bonds (edges) into atomistic graph representations, our method efficiently extracts orbital onsite energies and inter-orbital hopping parameters through supervised learning of density functional theory-derived band structures. The physics-aware architecture of GNNs, which inherently mirrors the atomic and bonding configurations of materials, ensures that the predicted TB models retain intrinsic physical interpretability, including the sparse matrix form, tunable orbital localization, exponentially decaying hopping strength, and defect-resolved local density of states, significantly broadening their applicability. As a result, it allows co-training on defective and defect-free systems, so that structural perturbations are naturally encoded as parameter changes, overcoming the lack of hopping parameters between distinct configurations and enabling the construction of Hamiltonians for non-periodic, defect-containing device channels, a longstanding challenge in ab initio quantum transport modeling. Furthermore, we develop the model size scaling and band structure editing functionalities, enabling flexible manipulation of electronic properties and cutting computational costs. We apply this framework to amorphous In-Ga-Zn-O and 4H-silicon carbide, two technologically critical channel materials whose performance-limiting defects cause unresolved reliability issues such as current degradation and threshold voltage drift, arising from quantum effects that cannot be captured by conventional drift-diffusion models. This work bridges the gap between ab initio calculations and device-level modeling, offering a transformative tool for semiconductor device design and beyond. Physical sciences/Materials science/Theory and computation/Atomistic models Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Physics/Electronics, photonics and device physics/Electronic and spintronic devices Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review 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. 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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-7831376","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":532188820,"identity":"8227e0c3-7bfc-49e3-ad1b-b41faf2c0dc3","order_by":0,"name":"Lei 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Here, we propose a graph neural network (GNN)-based framework for automated TB model construction. By integrating atomic sites (nodes) and chemical bonds (edges) into atomistic graph representations, our method efficiently extracts orbital onsite energies and inter-orbital hopping parameters through supervised learning of density functional theory-derived band structures. The physics-aware architecture of GNNs, which inherently mirrors the atomic and bonding configurations of materials, ensures that the predicted TB models retain intrinsic physical interpretability, including the sparse matrix form, tunable orbital localization, exponentially decaying hopping strength, and defect-resolved local density of states, significantly broadening their applicability. 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