Metal-organic frameworks meet Uni-MOF: a transformer-based gas adsorption detector

Metal-organic frameworks meet Uni-MOF: a transformer-based gas adsorption detector | 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 Metal-organic frameworks meet Uni-MOF: a transformer-based gas adsorption detector Jingqi Wang, Jiapeng Liu, Hongshuai Wang, Guolin Ke, Linfeng Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2923144/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Mar, 2024 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Gas separation is crucial for industrial production and environmental protection, with metal-organic frameworks(MOFs) offering a promising solution due to their tunable structural properties and chemical compositions. Traditional simulation approaches, such as molecular dynamics, are complex and computationally demanding. Although feature engineering-based machine learning methods perform better, they are susceptible to overfitting because of limited labeled data. Furthermore, these methods are typically designed for single tasks, such as predicting gas adsorption capacity under specific conditions, which restricts the utilization of comprehensive datasets including all adsorption capacities. To address these challenges, we propose Uni-MOF, an innovative framework for large-scale, three-dimensional MOF representation learning, designed for universal multi-gas prediction. Specifically, Uni-MOF serves as a versatile "gas adsorption detector" for MOF materials, employing pure three-dimensional representations learned from over 631,000 collected MOF and COF structures. Our experimental results show that Uni-MOF can automatically extract structural representations and predict adsorption capacities under various operating conditions using a single model. For simulated data, Uni-MOF exhibits remarkably high predictive accuracy across all datasets. Impressively, the values predicted by Uni-MOF correspond with the outcomes of adsorption experiments. Furthermore, Uni-MOF demonstrates considerable potential for broad applicability in predicting a wide array of other properties. Physical sciences/Materials science/Theory and computation/Computational methods Physical sciences/Mathematics and computing/Scientific data Full Text Additional Declarations There is NO Competing Interest. Supplementary Files UniMOFSI.pdf Uni-MOF Supplementary Information Cite Share Download PDF Status: Published Journal Publication published 01 Mar, 2024 Read the published version in Nature Communications → 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-2923144","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":202821106,"identity":"45dcb089-bd45-4101-a814-696eea2d0848","order_by":0,"name":"Jingqi Wang","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jingqi","middleName":"","lastName":"Wang","suffix":""},{"id":202821108,"identity":"03e82792-79fe-469d-a7c3-680e0c8d7c4e","order_by":1,"name":"Jiapeng Liu","email":"","orcid":"https://orcid.org/0000-0001-8667-1929","institution":"Sun Yat-Sen 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