Data-Driven De Novo Design of Super-Adhesive Hydrogels

Data-Driven De Novo Design of Super-Adhesive Hydrogels | 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 Physical Sciences - Article Data-Driven De Novo Design of Super-Adhesive Hydrogels Jian Ping Gong, Hongguang Liao, Sheng Hu, Hu Yang, Lei Wang, Shinya Tanaka, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5491059/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Aug, 2025 Read the published version in Nature → Version 1 posted You are reading this latest preprint version Abstract Data-driven methodologies have revolutionized the discovery and prediction of new hard materials, such as crystal structures and high-entropy alloys1-5. However, their application to soft materials remains challenging due to the inherent complexity of their structure–property relationships6-8. Here, we present a comprehensive data-driven approach that integrates data mining, experimentation, and machine learning to develop high-performance adhesive hydrogels from scratch, tailored for demanding underwater environments. By leveraging protein databases, we devised a descriptor strategy to statistically replicate protein sequence patterns via ideal random copolymerization, enabling targeted hydrogel design and dataset construction. Using machine learning, we optimized hydrogel formulations even with a small dataset, achieving unprecedented adhesive performance. These super-adhesive hydrogels demonstrate immense potential across diverse applications, from biomedical engineering to deep-sea exploration, marking a significant advancement in the data-driven innovation for soft materials. Physical sciences/Materials science/Soft materials/Gels and hydrogels Physical sciences/Chemistry/Cheminformatics Full Text Additional Declarations Yes there is potential Competing Interest. H.L., S.H., I.T., W.L., H.F., and J.P.G. are inventors of a patent application (2024-134812) entitled “Random copolymers and adhesives” submitted by Hokkaido University, which covers the composition of underwater adhesive materials in this study. Supplementary Files DataS1.Consensussequencefragmentof200species.pdf Extended Data 1 DataS2.Pairwisecountingoffunctionalclassesinconsensussequencefragmentof200species.pdf Extended Data 2 adhesivehydrogelsmachinelearningSI.docx SUPPLEMENTARY INFORMATION SupplementaryTable2.xlsx Supplementary Table 2 SupplementaryTable4.xlsx Supplementary Table 4 SupplementaryVideo1.mp4 Supplementary Video 1 SupplementaryVideo2.mp4 Supplementary Video 2 SupplementaryVideo3.mp4 Supplementary Video 3 Cite Share Download PDF Status: Published Journal Publication published 06 Aug, 2025 Read the published version in Nature → 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. 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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-5491059","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Physical Sciences - Article","associatedPublications":[],"authors":[{"id":383010987,"identity":"eab5f34e-b778-45e0-bd86-622df2366c1b","order_by":0,"name":"Jian Ping 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