Accelerating materials discovery for water crisis: Multi-objective machine learning for atmospheric water harvesting by MOFs

Accelerating materials discovery for water crisis: Multi-objective machine learning for atmospheric water harvesting by MOFs | 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 materials discovery for water crisis: Multi-objective machine learning for atmospheric water harvesting by MOFs Fatemeh Keshavarz, Charalampos Livas, Emmanuel Tylianakis, Bernardo Barbiellini, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7765200/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 Addressing the global water crisis requires efficient water supply solutions. Metal-organic frameworks (MOFs) offer promise for atmospheric water harvesting (AWH). However, many MOFs suffer from poor water stability or limited adsorption capacity. To accelerate discovery, we conceptualize structure–property relationships and develop an artificial intelligence-based multi-objective workflow that evaluates MOF water uptake at low and high relative humidity, water selectivity, and stability. A wide range of classification and regression models, hyperparameter spaces, and feature selection methods are tested, with the light gradient boosting machine (LGBM) model achieving the best performance. Results reveal that water uptake and selectivity depend mainly on structural features while chemical features dominate stability. The workflow is validated on benchmark water-harvesting MOFs and newly reported stable structures. We identify the top 100 MOFs as leading AWH candidates and propose design rules to guide experimental efforts and new research directions. The workflow is available as AquaMOF, a user-friendly software package with a web interface ( https://aquamof.website/ ), enabling on-the-fly predictions of the AWH potential of new MOFs. Physical sciences/Materials science/Materials for energy and catalysis/Metal–organic frameworks Physical sciences/Materials science/Theory and computation/Computational methods Physical sciences/Physics/Chemical physics Physical sciences/Engineering/Chemical engineering Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SIcodes.zip Supplementary codes SItop100MOFs.csv Top 100 MOFs SIMLmanuscriptAWH.pdf Supplementary Information SIdatasets.zip Datases 1 and 2 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. 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-7765200","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":525585353,"identity":"89217a95-69fa-4b36-aa23-bab1cba6ceca","order_by":0,"name":"Fatemeh 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