Machine Learning Assisted Prediction of Dynamics in Current-Driven Nested Skyrmion Bags

Machine Learning Assisted Prediction of Dynamics in Current-Driven Nested Skyrmion Bags | 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 Machine Learning Assisted Prediction of Dynamics in Current-Driven Nested Skyrmion Bags Chendong Jin, Rui Li, Yuge Zhu, Xinyu Zhang, Mengting Li, Xingqiang Shi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7650437/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Apr, 2026 Read the published version in Communications Physics → Version 1 posted You are reading this latest preprint version Abstract Nested skyrmion bags, characterized by arbitrary integer topological charges ( Q ), hold considerable potential for application in spintronic devices. Through using micromagnetic simulations, we investigated the spin-orbit torque-driven dynamics of nested skyrmion bags, with calculation results consistent with Thiele equation analysis. For zero- Q bags with varying nesting configurations, our calculations consistently reveal motion with a zero skyrmion Hall angle. Although an increase in the number of internally nested skyrmions induces transverse elongation of the bag due to enhanced internal Magnus forces, we showed that such deformation can be suppressed by strengthening the confinement potential through either increasing the number of outermost domain wall layers or utilizing nanotrack boundaries. Furthermore, we evaluated twelve machine learning models for predicting the skyrmion Hall angle in nested skyrmion bags with arbitrary Q . Gradient boosting models and Neural Network achieved high predictive accuracy across a wide angular range, while the marked failure of linear regression unequivocally confirms the underlying nonlinearity of the physical relationships. Leveraging this predictive capability, we designed a corresponding nested skyrmion bags-based demultiplexer device. Our work enhances the understanding of current-driven dynamics in nested topological structures and offers practical insights for their use in spintronic applications such as racetrack memory and signal-routing devices. Physical sciences/Physics/Condensed-matter physics/Spintronics Physical sciences/Physics/Condensed-matter physics/Magnetic properties and materials Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Supplementarymaterial.pdf Supplementary Material for “Machine Learning Assisted Prediction of Dynamics in Current-Driven Nested Skyrmion Bags” SupplementaryVideoI.mp4 Supplementary Video I SupplementaryVideoII.mp4 Supplementary Video II SupplementaryvideoIII.mp4 Supplementary video III SupplementaryvideoIV.mp4 Supplementary video IV Cite Share Download PDF Status: Published Journal Publication published 30 Apr, 2026 Read the published version in Communications Physics → 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. 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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-7650437","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":520636059,"identity":"8307341e-ad30-4a81-bad1-290be308d18c","order_by":0,"name":"Chendong 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Bags","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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7650437/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7650437/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNested skyrmion bags, characterized by arbitrary integer topological charges (\u003cem\u003eQ\u003c/em\u003e), hold considerable potential for application in spintronic devices. Through using micromagnetic simulations, we investigated the spin-orbit torque-driven dynamics of nested skyrmion bags, with calculation results consistent with Thiele equation analysis. For zero-\u003cem\u003eQ\u003c/em\u003e bags with varying nesting configurations, our calculations consistently reveal motion with a zero skyrmion Hall angle. Although an increase in the number of internally nested skyrmions induces transverse elongation of the bag due to enhanced internal Magnus forces, we showed that such deformation can be suppressed by strengthening the confinement potential through either increasing the number of outermost domain wall layers or utilizing nanotrack boundaries. Furthermore, we evaluated twelve machine learning models for predicting the skyrmion Hall angle in nested skyrmion bags with arbitrary \u003cem\u003eQ\u003c/em\u003e. Gradient boosting models and Neural Network achieved high predictive accuracy across a wide angular range, while the marked failure of linear regression unequivocally confirms the underlying nonlinearity of the physical relationships. Leveraging this predictive capability, we designed a corresponding nested skyrmion bags-based demultiplexer device. Our work enhances the understanding of current-driven dynamics in nested topological structures and offers practical insights for their use in spintronic applications such as racetrack memory and signal-routing devices.\u003c/p\u003e","manuscriptTitle":"Machine Learning Assisted Prediction of Dynamics in Current-Driven Nested Skyrmion Bags","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 14:22:03","doi":"10.21203/rs.3.rs-7650437/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-physics","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsphys","sideBox":"Learn more about [Communications Physics](http://www.nature.com/commsphys/)","snPcode":"","submissionUrl":"","title":"Communications Physics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"87ce3fb0-dbca-4aa5-814f-9ea32f27fe39","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55343047,"name":"Physical sciences/Physics/Condensed-matter physics/Spintronics"},{"id":55343048,"name":"Physical sciences/Physics/Condensed-matter physics/Magnetic properties and materials"}],"tags":[],"updatedAt":"2026-05-18T17:18:06+00:00","versionOfRecord":{"articleIdentity":"rs-7650437","link":"https://doi.org/10.1038/s42005-026-02660-1","journal":{"identity":"communications-physics","isVorOnly":false,"title":"Communications Physics"},"publishedOn":"2026-05-01 00:00:00","publishedOnDateReadable":"May 1st, 2026"},"versionCreatedAt":"2025-10-08 14:22:03","video":"","vorDoi":"10.1038/s42005-026-02660-1","vorDoiUrl":"https://doi.org/10.1038/s42005-026-02660-1","workflowStages":[]},"version":"v1","identity":"rs-7650437","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7650437","identity":"rs-7650437","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}