Study on the Mechanical Properties and Critical Temperature of FeNiCrMn Alloy Using MD–ML–MA Framework

Study on the Mechanical Properties and Critical Temperature of FeNiCrMn Alloy Using MD–ML–MA Framework | 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 Study on the Mechanical Properties and Critical Temperature of FeNiCrMn Alloy Using MD–ML–MA Framework Jing Liu, Jinyuan Mao, Bin Wang, Qiankun Wang, Nan Zhang, Shiyi Pan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7615847/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Nov, 2025 Read the published version in Journal of Molecular Modeling → Version 1 posted 4 You are reading this latest preprint version Abstract Context and results The FeNiCrMn alloy gasket is vital for the sealing performance of the engine cylinder head–block interface and thus engine reliability. The transition temperature at which the plastic region disappears in the FeNiCrMn alloy gasket remains ambiguous.Molecular Dynamics (MD) simulations show that lowering temperature suppresses plastic deformation under tension but improves compressive performance, while strain rate has negligible effects on elastic and strength properties. Based on MD data, a Machine Learning (ML) model achieved high prediction accuracy (MAE = 0.0072, R 2 = 0.9949). Mathematical Analysis (MA) further identified critical temperatures of T textsubscript{\textit{c}} = \SI{509}{K} (tension) and \SI{526}{K} (compression), beyond which tensile plasticity vanishes and compressive behavior exhibits the opposite trend. Methods A combined MD–ML–MA framework was employed to investigate the mechanical properties and critical temperature of the FeNiCrMn alloy gasket. MD simulations assessed tensile and compressive responses across temperatures and strain rates. The resulting dataset was used to train an ML neural network with backpropagation algorithm for predictive modeling, while MA quantified the plastic region m(T) , enabling determination of critical temperature thresholds. FeNiCrMn Alloy Gasket MD ML MA Framework Mechanical Properties Critical Temperature Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Nov, 2025 Read the published version in Journal of Molecular Modeling → Version 1 posted Editorial decision: Revision requested 17 Sep, 2025 Editor assigned by journal 16 Sep, 2025 Submission checks completed at journal 16 Sep, 2025 First submitted to journal 14 Sep, 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-7615847","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":516325454,"identity":"ac1b1d0d-dee3-4907-888e-89dc11468e2b","order_by":0,"name":"Jing Liu","email":"","orcid":"","institution":"BYD (China)","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Liu","suffix":""},{"id":516325456,"identity":"39f24b5e-eb3e-41c9-8ccf-596665d0478f","order_by":1,"name":"Jinyuan 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The transition temperature at which the plastic region disappears in the FeNiCrMn alloy gasket remains ambiguous.Molecular Dynamics (MD) simulations show that lowering temperature suppresses plastic deformation under tension but improves compressive performance, while strain rate has negligible effects on elastic and strength properties. Based on MD data, a Machine Learning (ML) model achieved high prediction accuracy (MAE = 0.0072, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.9949). Mathematical Analysis (MA) further identified critical temperatures of \u003cem\u003eT\u003c/em\u003etextsubscript{\\textit{c}} = \\SI{509}{K} (tension) and \\SI{526}{K} (compression), beyond which tensile plasticity vanishes and compressive behavior exhibits the opposite trend. \u003cb\u003eMethods\u003c/b\u003e A combined MD\u0026ndash;ML\u0026ndash;MA framework was employed to investigate the mechanical properties and critical temperature of the FeNiCrMn alloy gasket. MD simulations assessed tensile and compressive responses across temperatures and strain rates. The resulting dataset was used to train an ML neural network with backpropagation algorithm for predictive modeling, while MA quantified the plastic region \u003cem\u003em(T)\u003c/em\u003e, enabling determination of critical temperature thresholds.\u003c/p\u003e","manuscriptTitle":"Study on the Mechanical Properties and Critical Temperature of FeNiCrMn Alloy Using MD–ML–MA Framework","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-07 18:57:27","doi":"10.21203/rs.3.rs-7615847/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-17T06:54:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-16T22:08:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-16T22:08:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Molecular Modeling","date":"2025-09-15T03:29:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-modeling","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmmo","sideBox":"Learn more about [Journal of Molecular Modeling](https://www.springer.com/journal/894)","snPcode":"894","submissionUrl":"https://submission.nature.com/new-submission/894/3","title":"Journal of Molecular Modeling","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7001c848-8b6d-4453-b7c6-c0da019ac697","owner":[],"postedDate":"October 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T16:06:32+00:00","versionOfRecord":{"articleIdentity":"rs-7615847","link":"https://doi.org/10.1007/s00894-025-06575-6","journal":{"identity":"journal-of-molecular-modeling","isVorOnly":false,"title":"Journal of Molecular Modeling"},"publishedOn":"2025-11-22 15:58:22","publishedOnDateReadable":"November 22nd, 2025"},"versionCreatedAt":"2025-10-07 18:57:27","video":"","vorDoi":"10.1007/s00894-025-06575-6","vorDoiUrl":"https://doi.org/10.1007/s00894-025-06575-6","workflowStages":[]},"version":"v1","identity":"rs-7615847","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7615847","identity":"rs-7615847","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}