Predictive Design of Ultrastretchable Electrodes with Strain-Insensitive Performance via Machine Intelligence

Predictive Design of Ultrastretchable Electrodes with Strain-Insensitive Performance via Machine Intelligence | 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 Predictive Design of Ultrastretchable Electrodes with Strain-Insensitive Performance via Machine Intelligence Po-Yen Chen, Haochen Yang, Qiongyu Chen, Tianle Chen, Yang Li, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4953573/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 Innovations in wearable electronics and soft robotics hinge significantly on the development of stretchable electrodes. However, a persistent challenge lies in balancing high stretchability, functional performance, and strain insensitivity. Traditional methods rely on time-consuming and labor-intensive iterative experiments to navigate a vast parameter space. To overcome this, we establish an integrated workflow merging collaborative robotics, machine learning, and finite element simulations to enable the predictive design of ultrastretchable electrodes with strain-insensitive performance. Initially, an automated pipetting robot generates 286 nanocomposites, and their electrical conductance is assessed to train a support-vector machine regressor. Through 7 active learning loops, we fabricate 146 conductive interlayers to construct an ensemble model of artificial neural networks. Leveraging the prediction model and two-scale simulations, we discover a microtextured conductive interlayer as a strain-stable platform. By evaporating a thin gold layer, we develop an ultrastretchable gold conductor with metal-like conductivity, surpassing 1,000% resistance-insensitive stretchability, and robust durability. Furthermore, electrodepositing Zn and MnO2 on gold conductors enables fabrication of a Zn//MnO2 battery showcasing >300% stretchability and strain-insensitive performance. This machine intelligence-driven approach expedites the multi-parameter optimization of stretchable electrodes, achieving strain-invariant functionalities. Physical sciences/Materials science/Structural materials/Composites Physical sciences/Engineering/Mechanical engineering Conductive interlayers machine learning collaborative robotics stretchable gold conductors strain-resilient zinc batteries Full Text Additional Declarations There is NO Competing Interest. Supplementary Files MovieS1.mp4 Automated pipetting robot for preparing various MXene/SWNT/AuNP/PVA mixtures MovieS2.mp4 FE simulation of G2–2D1D and G2–2D2D stretchable nanocomposites under uniaxial elongations in top and perspective views SI0821Final.pdf Supporting Information - Predictive Design of Ultrastretchable Electrodes with Strain-Insensitive Performance via Machine Intelligence 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. 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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-4953573","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":361158022,"identity":"45e163ae-4f0c-4330-aabf-0531109256ae","order_by":0,"name":"Po-Yen 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