New insight into viscosity prediction of imidazolium-based ionic liquids and their mixtures with machine learning models

New insight into viscosity prediction of imidazolium-based ionic liquids and their mixtures with machine learning models | 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 New insight into viscosity prediction of imidazolium-based ionic liquids and their mixtures with machine learning models Amir Hossein Sheikhshoaei, Ali Sanati This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6354705/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Ionic liquids (ILs) as eco-friendly solvents have attracted particular attention in various fields of science including the petroleum industry. Among the different families of ILs, imidazolium-based ILs have been the subject of many research studies. However, not enough experimental studies were conducted to determine the viscosity of this family of ILs. Therefore, accurate viscosity prediction is crucial for their practical applications. This study aims to predict the viscosity of imidazolium-based ILs and their mixtures using critical properties of these ILs as input parameters. To achieve this, machine learning (ML) models have been implemented. Furthermore, the performance of these ML models in predicting the viscosity of IL mixtures was compared with a Molecular-based model, ePC-SAFT-FVT (ePC-FVT-MB), and an Ion-based model, ePC-SAFT-FVT (ePC-FVT-MB). Graphical and statistical analyses revealed that the RF model offers the lowest error for viscosity prediction of pure ILs, while CatBoost performs the best for IL mixtures. In addition, sensitivity analysis showed that viscosity decreases with temperature and increases with pressure. The proposed models exhibit high accuracy under varying conditions. Outlier detection using the Leverage method indicated that 95.11% of pure IL viscosity data and 94.92% of mixed ILs viscosity data are statistically valid. Physical sciences/Chemistry/Chemical engineering Physical sciences/Chemistry/Green chemistry Machine learning models viscosity imidazolium-based ionic liquids graphical analysis statistical analysis Full Text Additional Declarations No competing interests reported. Supplementary Files MLViscositySupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 May, 2025 Reviews received at journal 03 May, 2025 Reviews received at journal 03 May, 2025 Reviews received at journal 26 Apr, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviewers agreed at journal 19 Apr, 2025 Reviewers invited by journal 17 Apr, 2025 Editor assigned by journal 14 Apr, 2025 Editor invited by journal 14 Apr, 2025 Submission checks completed at journal 11 Apr, 2025 First submitted to journal 01 Apr, 2025 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. 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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-6354705","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":447397694,"identity":"c50623e8-7395-4918-aa44-51fc49f07094","order_by":0,"name":"Amir Hossein Sheikhshoaei","email":"","orcid":"","institution":"Hakim Sabzevari University","correspondingAuthor":false,"prefix":"","firstName":"Amir","middleName":"Hossein","lastName":"Sheikhshoaei","suffix":""},{"id":447397695,"identity":"c371eb2b-f30c-4295-a019-e2fb7c781539","order_by":1,"name":"Ali Sanati","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBADHn4JKIuNSB0GPJIzSNXCYHCDWAfxN7A//FxQ80fG+Hb7MwmGGjsGPukD+LVIHOAxlp5xzIDH7M4ZMwmGY8kMbHwJBKw5wMMgzcMG1HIjh02Cge0AAxsPAR3yB9gf/+b5Z8BjPCMd6LB/RGgxOMBgJs3bZsBjIJFgJsHYRoQWw8M8Zta8fcY8EjdyjC0S+5J5CGqRO97++DbPNzl7/hnpD298+GYnJ99DQAsDMzInAZgMCGkYBaNgFIyCUUAEAAD5bjHai2p7RwAAAABJRU5ErkJggg==","orcid":"","institution":"Hakim Sabzevari University","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"","lastName":"Sanati","suffix":""}],"badges":[],"createdAt":"2025-04-01 15:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6354705/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6354705/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-08947-7","type":"published","date":"2025-07-02T15:58:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86180298,"identity":"5adfbc1e-1136-4ab2-be11-0a045fe07927","added_by":"auto","created_at":"2025-07-07 16:22:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11154572,"visible":true,"origin":"","legend":"","description":"","filename":"MLViscosityMSR2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6354705/v1_covered_65e67f89-d7e2-4004-8774-a8322ca778f8.pdf"},{"id":81395653,"identity":"3004d612-32f8-44ab-a0b6-c2fad62d79aa","added_by":"auto","created_at":"2025-04-25 15:33:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":210877,"visible":true,"origin":"","legend":"","description":"","filename":"MLViscositySupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6354705/v1/e43db1a07cb4ca5fd5e173fd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"New insight into viscosity prediction of imidazolium-based ionic liquids and their mixtures with machine learning models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Machine learning models, viscosity, imidazolium-based ionic liquids, graphical analysis, statistical analysis","lastPublishedDoi":"10.21203/rs.3.rs-6354705/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6354705/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIonic liquids (ILs) as eco-friendly solvents have attracted particular attention in various fields of science including the petroleum industry. Among the different families of ILs, imidazolium-based ILs have been the subject of many research studies. However, not enough experimental studies were conducted to determine the viscosity of this family of ILs. Therefore, accurate viscosity prediction is crucial for their practical applications. This study aims to predict the viscosity of imidazolium-based ILs and their mixtures using critical properties of these ILs as input parameters. To achieve this, machine learning (ML) models have been implemented. Furthermore, the performance of these ML models in predicting the viscosity of IL mixtures was compared with a Molecular-based model, ePC-SAFT-FVT (ePC-FVT-MB), and an Ion-based model, ePC-SAFT-FVT (ePC-FVT-MB). Graphical and statistical analyses revealed that the RF model offers the lowest error for viscosity prediction of pure ILs, while CatBoost performs the best for IL mixtures. In addition, sensitivity analysis showed that viscosity decreases with temperature and increases with pressure. The proposed models exhibit high accuracy under varying conditions. 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