MOFBuilder: Automated end-to-end modeling ofMOF dynamics for high-throughput screening

MOFBuilder: Automated end-to-end modeling ofMOF dynamics for high-throughput screening | 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 MOFBuilder: Automated end-to-end modeling ofMOF dynamics for high-throughput screening Mårten S. G. Ahlquist, Chenxi Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8669004/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Apr, 2026 Read the published version in npj Computational Materials → Version 1 posted 10 You are reading this latest preprint version Abstract The vast chemical design space of Metal–Organic Frameworks (MOFs) offersunparalleled opportunities for targeted materials design, yet computationalscreening remains largely restricted to static structure derived from the CIFfile. We introduce MOFBuilder, a modular end-to-end pipeline that leveragesmolecular-level identities to automatically generate chemically consistent, molec-ular dynamics (MD) ready MOF models, flexibly supporting periodic, defective,cluster, and slab representations. By eliminating the manual effort typicallyrequired for model preparation, the pipeline enables a seamless construction ofcomplex systems ranging from large-scale bio-hybrid interfaces to functionalizedhigh-throughput libraries. As proof of the need for high-throughput dynamicmodeling, we show that dynamic screening is necessary to circumvent the "Poros-ity Paradox". Several functionalized UiO-66 variants classified as non-porous bystatic geometric analysis exhibit significant CO2 uptake through gate-openingmechanisms captured only via MD. By enabling the high-throughput generationof consistent, dynamic datasets, MOFBuilder addresses a critical gap in discoverypipelines and provides the foundation for more predictive, data-driven materialsdesign. Physical sciences/Chemistry Physical sciences/Engineering Physical sciences/Materials science metal-organic framework molecular dynamics Full Text Additional Declarations No competing interests reported. Supplementary Files MOFbuilder2.pdf Cite Share Download PDF Status: Published Journal Publication published 17 Apr, 2026 Read the published version in npj Computational Materials → Version 1 posted Editorial decision: Revision requested 03 Mar, 2026 Reviews received at journal 28 Feb, 2026 Reviews received at journal 25 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers agreed at journal 07 Feb, 2026 Reviewers invited by journal 07 Feb, 2026 Editor assigned by journal 03 Feb, 2026 Submission checks completed at journal 27 Jan, 2026 First submitted to journal 22 Jan, 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. 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-8669004","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":590449831,"identity":"7bb23546-fb87-462a-99d3-b75c7e46ce97","order_by":0,"name":"Mårten S. G. 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