Integrating Density Functional Theory, Finite Element Analysis & Machine Learning in the study of mechano-thermal properties of a Triclinic Pentafluorophenyl-Urea Derivative

Integrating Density Functional Theory, Finite Element Analysis & Machine Learning in the study of mechano-thermal properties of a Triclinic Pentafluorophenyl-Urea Derivative | 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 Integrating Density Functional Theory, Finite Element Analysis & Machine Learning in the study of mechano-thermal properties of a Triclinic Pentafluorophenyl-Urea Derivative Venkata Shivakumar Remella, Haridharan Neelamegan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6644234/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 The mechanical behavior of organic molecular crystals is a critical factor in their applicability for structural and functional materials. In this study, we present a first-principles computational framework for predicting the thermo-mechanical properties of PDPF (C 19 ​H 7 ​F 10 ​N 5 ​O 2 ​), integrating Density Functional Theory (DFT), Finite Element Analysis (FEA) for elasticity simulations, and Machine Learning (ML) regression models. A major challenge in using commercial Finite Element Analysis (FEA) software for organic molecular crystals is the lack of direct compatibility with Crystallographic Information Files (CIF). Standard FEA tools rely on predefined material databases and user-defined stress-strain models, making it difficult—or even impossible—to directly simulate a molecular crystal structure from its CIF file. To address this, we developed a CIF → DFT → Python-based FEA workflow, where DFT-derived elastic stiffness tensors (C ij ​) were used to define the material properties in a custom Python elasticity solver. This method eliminates the need for meshing and provides a self-consistent approach for simulating stress-strain behavior under uniaxial, shear, biaxial, triaxial, and hydrostatic loading conditions. The results reveal that PDPF exhibits high tensile strength (8.05 GPa) with brittle fracture, enhanced failure resistance in biaxial stress (9.57 GPa), and bulk collapse at -18.83 GPa under extreme compression. Furthermore, we implement Basquin’s Law for fatigue modeling and Norton’s Law for creep prediction, showing that PDPF maintains high endurance under cyclic stress but exhibits gradual softening under long-term deformation. This study provides a computationally self-consistent framework for predicting mechanical behavior in molecular crystals, while also identifying the limitations of molecular descriptors in mechanical modeling. Future work will focus on incorporating molecular dynamics (MD) simulations, defect modeling, and advanced ML architectures to improve predictive accuracy. Materials Chemistry Mechanical behaviour organic crystals thermo-mechanical properties Density functional theory Finite Element Analysis elastic properties machine learning Full Text Additional Declarations The authors declare no competing interests. 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-6644234","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":455187803,"identity":"7383ba3d-19cb-4ecc-81a8-d80a194d75ee","order_by":0,"name":"Venkata Shivakumar Remella","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYHACgwMJFTZyDAzMDUDOASBOACPcgI3B8MCHM2nGDAyMjQ3EajE+OLPtcGIDiha8rrrfvOEwb9vh9A3HG9sffKi5w8DPnmPA8HAHHi3H2AoO85xLz91w5mBj44xjzxgke94YMCSewa1Fso3H4DBPmXXuzBmJjc28DYcZDG4AbUlsI6SFjTldcv7Dxua/QC32hLTws/EYHJzR5pzAL8HY2MwIskWCoJa0AlAgG/bzJDbO7Dl2mEfizLOCA/i0sDEf3vwBGJXybOyHD3z4UXNYjr89eePDn3i0YAAeEHGABA2jYBSMglEwCrAAAAwsXBAWkFdIAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0005-7836-6525","institution":"Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Venkata","middleName":"Shivakumar","lastName":"Remella","suffix":""},{"id":455188190,"identity":"470ea5b2-bdb0-4b3d-a135-ca43df67bf28","order_by":1,"name":"Haridharan Neelamegan","email":"","orcid":"https://orcid.org/0000-0003-0737-9837","institution":"Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Haridharan","middleName":"","lastName":"Neelamegan","suffix":""}],"badges":[],"createdAt":"2025-05-12 08:06:57","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6644234/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6644234/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82671279,"identity":"5ffced1c-1b43-4724-aa41-d2e99fd7b55f","added_by":"auto","created_at":"2025-05-14 02:56:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1054419,"visible":true,"origin":"","legend":"","description":"","filename":"PDPFFEADFTMLPaper2Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6644234/v1_covered_6eea5e31-82f8-4df9-99af-3ab6f332bc35.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIntegrating Density Functional Theory, Finite Element Analysis \u0026amp; Machine Learning in the study of mechano-thermal properties of a Triclinic Pentafluorophenyl-Urea Derivative\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Advanced Training Technologies","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":"Mechanical behaviour, organic crystals, thermo-mechanical properties, Density functional theory, Finite Element Analysis, elastic properties, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-6644234/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6644234/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe mechanical behavior of organic molecular crystals is a critical factor in their applicability for structural and functional materials. In this study, we present a first-principles computational framework for predicting the thermo-mechanical properties of PDPF (C\u003csub\u003e19\u003c/sub\u003e​H\u003csub\u003e7\u003c/sub\u003e​F\u003csub\u003e10\u003c/sub\u003e​N\u003csub\u003e5\u003c/sub\u003e​O\u003csub\u003e2\u003c/sub\u003e​), integrating Density Functional Theory (DFT), Finite Element Analysis (FEA) for elasticity simulations, and Machine Learning (ML) regression models.\u003c/p\u003e \u003cp\u003eA major challenge in using commercial Finite Element Analysis (FEA) software for organic molecular crystals is the lack of direct compatibility with Crystallographic Information Files (CIF). Standard FEA tools rely on predefined material databases and user-defined stress-strain models, making it difficult\u0026mdash;or even impossible\u0026mdash;to directly simulate a molecular crystal structure from its CIF file. To address this, we developed a CIF \u0026rarr; DFT \u0026rarr; Python-based FEA workflow, where DFT-derived elastic stiffness tensors (C\u003csub\u003eij\u003c/sub\u003e​) were used to define the material properties in a custom Python elasticity solver. This method eliminates the need for meshing and provides a self-consistent approach for simulating stress-strain behavior under uniaxial, shear, biaxial, triaxial, and hydrostatic loading conditions.\u003c/p\u003e \u003cp\u003eThe results reveal that PDPF exhibits high tensile strength (8.05 GPa) with brittle fracture, enhanced failure resistance in biaxial stress (9.57 GPa), and bulk collapse at -18.83 GPa under extreme compression. Furthermore, we implement Basquin\u0026rsquo;s Law for fatigue modeling and Norton\u0026rsquo;s Law for creep prediction, showing that PDPF maintains high endurance under cyclic stress but exhibits gradual softening under long-term deformation.\u003c/p\u003e \u003cp\u003eThis study provides a computationally self-consistent framework for predicting mechanical behavior in molecular crystals, while also identifying the limitations of molecular descriptors in mechanical modeling. Future work will focus on incorporating molecular dynamics (MD) simulations, defect modeling, and advanced ML architectures to improve predictive accuracy.\u003c/p\u003e","manuscriptTitle":"Integrating Density Functional Theory, Finite Element Analysis \u0026amp; Machine Learning in the study of mechano-thermal properties of a Triclinic Pentafluorophenyl-Urea Derivative","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-14 02:32:35","doi":"10.21203/rs.3.rs-6644234/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"7aebb8e5-c2bb-4aac-9380-26e0988350f0","owner":[],"postedDate":"May 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48384940,"name":"Materials Chemistry"}],"tags":[],"updatedAt":"2025-05-14T02:32:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-14 02:32:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6644234","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6644234","identity":"rs-6644234","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}