Machine-Learned Interatomic Potential Insights into the Effect of H₂O on CO₂ Adsorption in HEU-Type Zeolites

Machine-Learned Interatomic Potential Insights into the Effect of H₂O on CO₂ Adsorption in HEU-Type Zeolites | 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 Research Article Machine-Learned Interatomic Potential Insights into the Effect of H₂O on CO₂ Adsorption in HEU-Type Zeolites Anthony Pembere, Fred Sifuna This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9475077/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 The rapid increase in atmospheric CO₂ concentrations remains a major driver of global climate change, making the development of high-performance carbon capture materials essential. These materials must be capable of operating efficiently under realistic conditions including humidity and industrial gases. Understanding the influence of water on CO₂ adsorption in zeolite structures is therefore crucial for developing robust carbon capture technologies. In this study, the impact of water on CO₂ adsorption in HEU-type zeolites is examined using a machine-learned interatomic potential (MLIP) trained on high-fidelity ab initio data. MLIPs enable atomistic simulations with near-density functional theory (DFT) accuracy while also providing access to extended timescales and lengths. Radial distribution functions (RDFs) and cumulative coordination numbers of HEU zeolite co-loaded with H₂O and CO₂ show that the structure is preserved, as indicated by the absence of changes in the Si–O and Si–Si peaks. The O–O distribution broadens, reflecting the aggregation of hydrogen-bonded water molecules within the pores. The reduction in C–network correlations indicates that water preferentially occupies the adsorption sites, displacing CO₂ and weakening its interaction with the network. These results highlight competitive adsorption and pore obstruction under humid conditions. Therefore, improving the hydrophobicity of the zeolite surface or pre-drying the gas streams may be necessary to maintain adsorption efficiency. Materials Theory and Modeling Full Text Additional Declarations The authors declare no competing interests. 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. 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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-9475077","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626499701,"identity":"a38d3b65-126d-4f15-abd8-e60b22a1bad7","order_by":0,"name":"Anthony Pembere","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYJACZhDBD8QHGBtAJLFaJBtI1mIAUkmUFv7ZzUc3F1TckzO+kZ144OcOBjm+Gwn4tUjcOZZ2e8aZYmOzG7kbDvaeYTCWJKSF4UaO2W3etoTEbUAthxnbGBI3ENIifyP/223efwn1m2dAtNQT1GJwI4ftNm9DQoKBBERLggEhLYZ3jpndnnEswXDGmbdAv7RJGM488wC/Frnbzc9uF9QkyPO3527+8LPNRp7vOAFbGCTwconRMgpGwSgYBaMAEwAAnBNP/bg7KwQAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-9819-9155","institution":"1 Department of Physical Sciences, Jaramogi Oginga Odinga University of Science and Technology, P.O Box 210 Bondo 40601, Kenya 2Michigan Institute for Data and AI in Society, University of Michigan. 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These materials must be capable of operating efficiently under realistic conditions including humidity and industrial gases. Understanding the influence of water on CO₂ adsorption in zeolite structures is therefore crucial for developing robust carbon capture technologies. In this study, the impact of water on CO₂ adsorption in HEU-type zeolites is examined using a machine-learned interatomic potential (MLIP) trained on high-fidelity ab initio data. MLIPs enable atomistic simulations with near-density functional theory (DFT) accuracy while also providing access to extended timescales and lengths. Radial distribution functions (RDFs) and cumulative coordination numbers of HEU zeolite co-loaded with H₂O and CO₂ show that the structure is preserved, as indicated by the absence of changes in the Si\u0026ndash;O and Si\u0026ndash;Si peaks. The O\u0026ndash;O distribution broadens, reflecting the aggregation of hydrogen-bonded water molecules within the pores. The reduction in C\u0026ndash;network correlations indicates that water preferentially occupies the adsorption sites, displacing CO₂ and weakening its interaction with the network. These results highlight competitive adsorption and pore obstruction under humid conditions. 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