Accelerating Sustainable Glass Discovery: Integrating Molecular Dynamics, Machine Learning, and Robotic Synthesis

Accelerating Sustainable Glass Discovery: Integrating Molecular Dynamics, Machine Learning, and Robotic Synthesis | 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 Accelerating Sustainable Glass Discovery: Integrating Molecular Dynamics, Machine Learning, and Robotic Synthesis Felix Arendt, Tina Waurischk, Stefan Reinsch, Andrea S. S. Camargo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9471529/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Discovering sustainable glass compositions demands navigating vast chemical spaces – a challenge that conventional experimentation cannot meet efficiently. Here we introduce the Simulation-Calibrated Active Learning Estimator (SCALE), a closed-loop framework uniting high-throughput molecular dynamics (MD), machine learning (ML), and robotic synthesis to bridge the gap between simulation and experiment. Over 42,000 melt–quench MD simulations with compact cells (≈ 200–500 atoms) train ML models that predict density and elastic moduli with cross-validated R 2 values up to 0.98. Comparison with 55 robotically synthesized sodium alumino-borosilicate glasses reveals systematic density overestimation of up to 5%. Rather than naively augmenting training data, SCALE iteratively learns a composition-dependent calibration from minimal targeted measurements. The protocol converges within two to four iterations of six measurements each, significantly reducing density errors and enabling glass optimization with fewer than 20 strategically chosen experiments. Physical sciences/Materials science Physical sciences/Mathematics and computing Physical sciences/Physics Full Text Additional Declarations No competing interests reported. Supplementary Files SCALESupportingInformation.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 13 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers invited by journal 29 Apr, 2026 Editor assigned by journal 29 Apr, 2026 Submission checks completed at journal 22 Apr, 2026 First submitted to journal 20 Apr, 2026 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-9471529","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":632947962,"identity":"bf05e51a-8bb4-459d-b191-0102cac877b1","order_by":0,"name":"Felix Arendt","email":"","orcid":"","institution":"Friedrich-Schiller University Jena","correspondingAuthor":false,"prefix":"","firstName":"Felix","middleName":"","lastName":"Arendt","suffix":""},{"id":632947963,"identity":"51df8133-35ae-47db-b472-05fb98404cd1","order_by":1,"name":"Tina Waurischk","email":"","orcid":"","institution":"Federal Institute for Materials Research and Testing (BAM)","correspondingAuthor":false,"prefix":"","firstName":"Tina","middleName":"","lastName":"Waurischk","suffix":""},{"id":632947964,"identity":"f9b9b431-b84e-480a-a8df-464aca0f4e91","order_by":2,"name":"Stefan Reinsch","email":"","orcid":"","institution":"Federal Institute for Materials Research and Testing (BAM)","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Reinsch","suffix":""},{"id":632947965,"identity":"4f496b39-ac6b-4508-8118-8d8e03f8a83b","order_by":3,"name":"Andrea S. 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Here we introduce the Simulation-Calibrated Active Learning Estimator (SCALE), a closed-loop framework uniting high-throughput molecular dynamics (MD), machine learning (ML), and robotic synthesis to bridge the gap between simulation and experiment. Over 42,000 melt\u0026ndash;quench MD simulations with compact cells (\u0026asymp;\u0026thinsp;200\u0026ndash;500 atoms) train ML models that predict density and elastic moduli with cross-validated \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e values up to 0.98. Comparison with 55 robotically synthesized sodium alumino-borosilicate glasses reveals systematic density overestimation of up to 5%. Rather than naively augmenting training data, SCALE iteratively learns a composition-dependent calibration from minimal targeted measurements. 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