Synergistic Modeling of Liquid Properties: Integrating Neural Network-Derived Molecular Features with Modified Kernel Models

Synergistic Modeling of Liquid Properties: Integrating Neural Network-Derived Molecular Features with Modified Kernel 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 Research Article Synergistic Modeling of Liquid Properties: Integrating Neural Network-Derived Molecular Features with Modified Kernel Models Hyuntae Lim, YounJoon Jung This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4221295/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 A significant challenge in applying machine learning to computational chemistry, particularly considering the growing complexity of contemporary machine learning models, is the scarcity of available experimental data. To address this issue, we introduce an approach that derives molecular features from an intricate neural network-based model and applies them to a simpler conventional machine learning model that is robust to overfitting. This method can be applied to predict various properties of a liquid system, including viscosity or surface tension, based on molecular features drawn from the ab initio-calculated free energy of solvation. Furthermore, we propose a modified kernel model that includes Arrhenius temperature dependence to incorporate theoretical principles and diminish extreme nonlinearity in the model. The modified kernel model demonstrated significant improvements in certain scenarios and possible extensions to various theoretical concepts of molecular systems. Full Text Additional Declarations No competing interests reported. Supplementary Files esi.pdf 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-4221295","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288795891,"identity":"fe026e70-d8be-4f82-8e26-31ed82935e63","order_by":0,"name":"Hyuntae Lim","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Hyuntae","middleName":"","lastName":"Lim","suffix":""},{"id":288795892,"identity":"29fef1f4-24f2-4b7d-94dc-fe949a3ce831","order_by":1,"name":"YounJoon Jung","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYFCCAwlAwgbGSyBaSxpJWsDgMAlaDA4eeCZd8Ou8vblEAuOHHwxp+YS1HDiQJj2z73bizhkJzJI9DDmWDURp4e25nWBwI4FBmoGhwoA4W3h7ztkDtTD/Jl4Lz48DjBtuJLABbckhrEXywIFka96G5MQNZx62WfYYpBHWwnfjTOJtnj929gbHkw/f+FGRTFiLwo0zCQyMbSAmYwPQnQQ1MDDI97cfYGD4Q4TKUTAKRsEoGLkAAKeUQM1Hl33JAAAAAElFTkSuQmCC","orcid":"","institution":"Seoul National University","correspondingAuthor":true,"prefix":"","firstName":"YounJoon","middleName":"","lastName":"Jung","suffix":""}],"badges":[],"createdAt":"2024-04-05 07:18:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4221295/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4221295/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57058048,"identity":"7c938440-4262-4eaf-9ca6-7873df169f8c","added_by":"auto","created_at":"2024-05-24 05:18:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2474673,"visible":true,"origin":"","legend":"","description":"","filename":"JCheminfo240320.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4221295/v1_covered_ec0bb569-be3b-4470-9697-48139616b150.pdf"},{"id":54465288,"identity":"f3a01a1d-5b64-4f93-a6ae-7e038d01a9b8","added_by":"auto","created_at":"2024-04-11 02:40:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":114246,"visible":true,"origin":"","legend":"","description":"","filename":"esi.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4221295/v1/7af5c008fe3daf281a7afbad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Synergistic Modeling of Liquid Properties: Integrating Neural Network-Derived Molecular Features with Modified Kernel Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4221295/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4221295/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"A significant challenge in applying machine learning to computational chemistry, particularly considering the growing complexity of contemporary machine learning models, is the scarcity of available experimental data. 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