GEMDAT: A Python Toolkit for Site-Resolved Diffusion Analysis in Solid-State Molecular Dynamics

GEMDAT: A Python Toolkit for Site-Resolved Diffusion Analysis in Solid-State Molecular Dynamics | 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 GEMDAT: A Python Toolkit for Site-Resolved Diffusion Analysis in Solid-State Molecular Dynamics Anastasia K. Lavrinenko, Theodosios Famprikis, Victor Landgraf, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8639958/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Molecular dynamics (MD) simulations have become essential for understanding diffusion mechanisms in solid-state materials, including ionic conductors, fuel cells, and gas sensors. Most existing studies and software packages, however, extract only basic information—tracer diffusivity and activation energies, usually assuming Arrhenius behaviour. But MD trajectories contain far more information than these standard metrics reveal. To unlock this hidden potential, we introduce GEMDAT—a user-friendly Python-based analysis tool designed to extract detailed diffusional properties from MD simulations of solid-state materials ( https://github.com/GEMDAT-repos/GEMDAT.git ). GEMDAT allows users to analyse the environment around atomic sites where species migration occurs and examine discrete jump events. The user can define these sites manually or allow GEMDAT to automatically identify them from MD simulation. Our tool provides access to vibrational amplitudes, site geometries, and site occupancies by mobile species–quantities that are also valuable for interpreting experimental diffraction data. In addition to basic properties such as mean-square displacements, radial distribution functions, and Arrhenius-based activation energies, GEMDAT calculates a wide range of diffusion-related properties, including jump rates, attempt frequencies, site-specific activation energies, collective motion, and rotational diffusion. To make the analysis workflow more efficient, GEMDAT caches data after the initial run, speeding up subsequent analyses, and generates visualizations for rapid interpretation of results. We demonstrate the application of GEMDAT on several case studies involving Li- and Na-ion conductors and plastic crystals. These examples showcase how the code extracts atomic-level structural features and connects them to macroscopic transport properties, guiding the optimization and development of solid-state materials. Physical sciences/Chemistry Physical sciences/Materials science Physical sciences/Physics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Mar, 2026 Reviews received at journal 02 Mar, 2026 Reviews received at journal 24 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviews received at journal 12 Feb, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviewers invited by journal 27 Jan, 2026 Editor assigned by journal 24 Jan, 2026 Submission checks completed at journal 22 Jan, 2026 First submitted to journal 19 Jan, 2026 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-8639958","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":582021744,"identity":"2f622dec-b4fa-44b0-9f3f-e0f0ab70438a","order_by":0,"name":"Anastasia K. 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Most existing studies and software packages, however, extract only basic information\u0026mdash;tracer diffusivity and activation energies, usually assuming Arrhenius behaviour. But MD trajectories contain far more information than these standard metrics reveal. To unlock this hidden potential, we introduce GEMDAT\u0026mdash;a user-friendly Python-based analysis tool designed to extract detailed diffusional properties from MD simulations of solid-state materials (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/GEMDAT-repos/GEMDAT.git\u003c/span\u003e\u003cspan address=\"https://github.com/GEMDAT-repos/GEMDAT.git\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). GEMDAT allows users to analyse the environment around atomic sites where species migration occurs and examine discrete jump events. The user can define these sites manually or allow GEMDAT to automatically identify them from MD simulation. Our tool provides access to vibrational amplitudes, site geometries, and site occupancies by mobile species\u0026ndash;quantities that are also valuable for interpreting experimental diffraction data. In addition to basic properties such as mean-square displacements, radial distribution functions, and Arrhenius-based activation energies, GEMDAT calculates a wide range of diffusion-related properties, including jump rates, attempt frequencies, site-specific activation energies, collective motion, and rotational diffusion. To make the analysis workflow more efficient, GEMDAT caches data after the initial run, speeding up subsequent analyses, and generates visualizations for rapid interpretation of results. We demonstrate the application of GEMDAT on several case studies involving Li- and Na-ion conductors and plastic crystals. These examples showcase how the code extracts atomic-level structural features and connects them to macroscopic transport properties, guiding the optimization and development of solid-state materials.\u003c/p\u003e","manuscriptTitle":"GEMDAT: A Python Toolkit for Site-Resolved Diffusion Analysis in Solid-State Molecular Dynamics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 18:18:25","doi":"10.21203/rs.3.rs-8639958/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-03T15:51:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T18:11:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-24T16:36:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T15:48:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-13T04:35:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215460598578613655314884671164856391777","date":"2026-01-29T18:23:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69764782931138953169033272858977075637","date":"2026-01-29T00:31:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140793826969848144414753115097489725673","date":"2026-01-28T19:38:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303619342403297455074340860313415871214","date":"2026-01-27T17:11:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-27T17:05:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-25T01:07:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-22T05:44:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Computational Materials","date":"2026-01-19T13:08:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-computational-materials","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"npjcompumats","sideBox":"Learn more about [npj Computational Materials](http://www.nature.com/npjcompumats/)","snPcode":"41524","submissionUrl":"https://mts-npjcompumats.nature.com/","title":"npj Computational Materials","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d12444d-31f2-4ddb-8ff7-4b50fc422695","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61923323,"name":"Physical sciences/Chemistry"},{"id":61923324,"name":"Physical sciences/Materials science"},{"id":61923325,"name":"Physical sciences/Physics"}],"tags":[],"updatedAt":"2026-05-04T12:08:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 18:18:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8639958","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8639958","identity":"rs-8639958","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}