Non-Random Parameterized Networks for Cross-Scale Modeling of Compositional Interplay

Non-Random Parameterized Networks for Cross-Scale Modeling of Compositional Interplay | 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 Non-Random Parameterized Networks for Cross-Scale Modeling of Compositional Interplay Shaodong Zhou, Jinming Fan, Chao Qian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6296481/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 Accurate prediction of molecular properties in complex chemical systems is crucial for accelerating materials discovery and chemical innovation. However, current computational methods often struggle to capture the intricate compositional interplay across multiple scales, from intramolecular bonds to intermolecular forces. Here, we introduce MesoNet, a novel non-random parameterized network specifically designed for cross-scale modeling. MesoNet's innovation lies in the construction of Non-Random Parameters (NRPs) – dynamically enriched atomic descriptors generated via Neural Circuit Policies (NCPs). NRPs uniquely capture both intrinsic atomic properties and their dynamic compositional context. Subsequently, these NRPs are processed through a hierarchical cross-scale message passing mechanism that explicitly integrates intra- and intermolecular interactions, essential for representing compositional interplay. Comprehensive evaluations across diverse datasets demonstrate that MesoNet achieves significantly superior predictive accuracy and enhanced chemical interpretability for molecular properties in both single and multi-component systems compared to existing methods. This work establishes a powerful and interpretable approach for cross-scale modeling of compositional complexity, aiming at advanced chemical simulations and design. Physical sciences/Chemistry/Cheminformatics Physical sciences/Engineering/Chemical engineering Full Text Additional Declarations There is NO Competing Interest. 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. 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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-6296481","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":444322371,"identity":"41e4db3e-b5c6-4aea-85de-dea37b18760c","order_by":0,"name":"Shaodong Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYFACHiCuYGAGMSVI0HKGZC2MbRAmcVrk+88efMw7r47d4ADzwds8DHZ5BLUYHDiXbMy7jY3Z4ABbsjUPQ3IxYS2MPWbSvNt4gFp4zKR5GA4kNhB0WDNQJe8cCaAW/m/EaWE4BtLSYACyhY04LQZneIwN5xxLYJY8zGZsOccgmQiH9Z8xfPCmpi6Z73jzwxtvKuyIcBgUJEMi04BY9UBgR4LaUTAKRsEoGGkAAGV6MDyxQ25uAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3048-4678","institution":"Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Shaodong","middleName":"","lastName":"Zhou","suffix":""},{"id":444322372,"identity":"3045154e-1e6a-4860-b351-df4a154ed71b","order_by":1,"name":"Jinming Fan","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Jinming","middleName":"","lastName":"Fan","suffix":""},{"id":444322373,"identity":"48d77446-dd71-47d9-bc5d-1894b8aee74f","order_by":2,"name":"Chao Qian","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Qian","suffix":""}],"badges":[],"createdAt":"2025-03-24 14:45:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6296481/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6296481/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85902047,"identity":"01e85b4a-7850-40d3-97c0-59d9d1eaf571","added_by":"auto","created_at":"2025-07-03 02:34:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1421168,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptMesoNetr0.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6296481/v1_covered_caa617f5-9ce7-45b1-ba07-4a66a8e23a2a.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Non-Random Parameterized Networks for Cross-Scale Modeling of Compositional Interplay","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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-6296481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6296481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurate prediction of molecular properties in complex chemical systems is crucial for accelerating materials discovery and chemical innovation. 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