FAIR-MOFs: Structure-centred synthesis inference from three-dimensional structures of metal-organic frameworks

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FAIR-MOFs: Structure-centred synthesis inference from three-dimensional structures of metal-organic frameworks | 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 FAIR-MOFs: Structure-centred synthesis inference from three-dimensional structures of metal-organic frameworks Thomas Heine, Dinga Wonanke, Antonio Longa, Asha Pankajakshan, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8375247/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 Metal-organic frameworks (MOFs) hold great promise for applications ranging from CO2 capture to atmospheric water harvesting, yet their discovery is hindered by limited access to reproducible and scalable synthesis. Here, we introduce FAIR-MOFs, a data-driven framework that directly links 3D crystal structures to experimentally validated synthetic routes. FAIR-MOFs combines a graph neural network that predicts essential synthesis precursors from structure alone with a retrosynthetic recommender that learns reagent co-occurrence patterns from literature. The framework is built on over 47,000 curated experimentally synthesised structures integrated through automated structural curation and building-unit recognition to maintain data integrity and FAIR principles. We demonstrate its predictive power by experimentally synthesising three hypothetical MOFs using conditions proposed by the model. Overall, FAIR-MOFs introduces a structure-centred approach to synthetic planning by demonstrating that synthetic precursors can be inferred directly from three-dimensional crystal structures of metalorganic frameworks. Physical sciences/Chemistry/Materials chemistry/Metal–organic frameworks Physical sciences/Chemistry/Theoretical chemistry/Method development Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SI.pdf Supplementary Information 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. 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-8375247","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":578345529,"identity":"a82bf30e-6c50-45c4-8f92-de9e114e8dcd","order_by":0,"name":"Thomas 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