Deep learning models to predict CO 2 solubility in imidazolium-based ionic liquids

Deep learning models to predict CO 2 solubility in imidazolium-based ionic liquids | 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 Deep learning models to predict CO 2 solubility in imidazolium-based ionic liquids Amir Hossein Sheikhshoaei, Ali Sanati This is a preprint; it has not been peer reviewed by a journal.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-6224372/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract This study aims to predict CO 2 solubility in imidazolium-based ionic liquids using deep learning models with input parameters of critical pressure, critical temperature, molecular weight, and acentric factor. The models used in this work include Bayesian Neural Networks (BNN), Deep Neural Networks (DNN), Gradient Boosting Neural Networks (GrowNet), and Tabular Neural Networks (TabNet). The results obtained from this study are compared with two PC-SAFT models named cQC-PC-SAFT-MSA (1) and cQC-PC-SAFT-MSA (2), where deep learning models outperformed SAFT models. Based on graphical and statistical analyses, the GrowNet model, with a root mean square error of 0.0067 and a coefficient of determination of 0.9962, showed the least error compared to other models. In addition, Pearson correlation coefficient (PCC) and Shapley additive description (SHAP) analyses revealed that pressure (P) is a key parameter affecting the solubility of CO 2 in imidazolium-based ionic liquids and significantly affects the model performance. Physical sciences/Engineering/Chemical engineering Physical sciences/Physics Machine learning TabNet GrowNet CO2 solubility imidazolium-based ionic liquids Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 08 May, 2025 Reviews received at journal 05 May, 2025 Reviewers agreed at journal 28 Apr, 2025 Reviews received at journal 19 Apr, 2025 Reviews received at journal 07 Apr, 2025 Reviewers agreed at journal 30 Mar, 2025 Reviewers agreed at journal 23 Mar, 2025 Reviewers invited by journal 20 Mar, 2025 Editor assigned by journal 17 Mar, 2025 Editor invited by journal 17 Mar, 2025 Submission checks completed at journal 14 Mar, 2025 First submitted to journal 14 Mar, 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. 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-6224372","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":433816742,"identity":"5736e84a-6c04-49c4-b016-736e24464e88","order_by":0,"name":"Amir Hossein Sheikhshoaei","email":"","orcid":"","institution":"Hakim Sabzevari University","correspondingAuthor":false,"prefix":"","firstName":"Amir","middleName":"Hossein","lastName":"Sheikhshoaei","suffix":""},{"id":433816743,"identity":"3737b57f-6d24-4334-9b66-ebf652872aa1","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":"2025-03-14 07:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6224372/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6224372/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-12004-8","type":"published","date":"2025-07-21T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87756850,"identity":"202f286e-dfbf-4258-8388-b88500a9d3e2","added_by":"auto","created_at":"2025-07-28 16:09:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1613551,"visible":true,"origin":"","legend":"","description":"","filename":"CO2SolubilityMSR1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6224372/v1_covered_626619c1-1c71-4abd-8606-b2b7e069b2fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep learning models to predict CO 2 solubility in imidazolium-based ionic liquids","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, TabNet, GrowNet, CO2 solubility, imidazolium-based ionic liquids","lastPublishedDoi":"10.21203/rs.3.rs-6224372/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6224372/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to predict CO\u003csub\u003e2\u003c/sub\u003e solubility in imidazolium-based ionic liquids using deep learning models with input parameters of critical pressure, critical temperature, molecular weight, and acentric factor. The models used in this work include Bayesian Neural Networks (BNN), Deep Neural Networks (DNN), Gradient Boosting Neural Networks (GrowNet), and Tabular Neural Networks (TabNet). The results obtained from this study are compared with two PC-SAFT models named cQC-PC-SAFT-MSA (1) and cQC-PC-SAFT-MSA (2), where deep learning models outperformed SAFT models. Based on graphical and statistical analyses, the GrowNet model, with a root mean square error of 0.0067 and a coefficient of determination of 0.9962, showed the least error compared to other models. In addition, Pearson correlation coefficient (PCC) and Shapley additive description (SHAP) analyses revealed that pressure (P) is a key parameter affecting the solubility of CO\u003csub\u003e2\u003c/sub\u003e in imidazolium-based ionic liquids and significantly affects the model performance.\u003c/p\u003e","manuscriptTitle":"Deep learning models to predict CO 2 solubility in imidazolium-based ionic liquids","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-28 13:43:28","doi":"10.21203/rs.3.rs-6224372/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-08T09:48:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-05T09:36:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73449616775434554808460755777843663659","date":"2025-04-28T12:45:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-19T15:01:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-08T03:24:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223994274189565999992723674708472305602","date":"2025-03-30T09:07:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151717949515253315264135246297803371889","date":"2025-03-24T00:58:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-20T08:40:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-17T23:05:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-17T18:20:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-14T13:50:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-14T07:38:31+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"586dcc7f-cf74-4f2a-9000-08659d397b33","owner":[],"postedDate":"March 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46193533,"name":"Physical sciences/Engineering/Chemical engineering"},{"id":46193534,"name":"Physical sciences/Physics"}],"tags":[],"updatedAt":"2025-07-28T16:06:07+00:00","versionOfRecord":{"articleIdentity":"rs-6224372","link":"https://doi.org/10.1038/s41598-025-12004-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-21 15:57:58","publishedOnDateReadable":"July 21st, 2025"},"versionCreatedAt":"2025-03-28 13:43:28","video":"","vorDoi":"10.1038/s41598-025-12004-8","vorDoiUrl":"https://doi.org/10.1038/s41598-025-12004-8","workflowStages":[]},"version":"v1","identity":"rs-6224372","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6224372","identity":"rs-6224372","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}