GenGLem: a generative framework for capturing chemical cliffs in energetic materials

GenGLem: a generative framework for capturing chemical cliffs in energetic materials | 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 GenGLem: a generative framework for capturing chemical cliffs in energetic materials Huajie Liu, Wenjing Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8611356/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract High-throughput prediction of explosive properties is currently bottlenecked by the prohibitive computational cost of potential energy surface (PES) sampling and the inability to account for complex environmental conditions. Here, we develop GenGLem, a dual-pathway generative framework featuring latent space exploration and microstructure guidance. GenGLem fundamentally transforms configurational sampling from traditional trajectory-based search into direct, end-to-end latent space generation. By integrating unconditional latent exploration and conditional diffusion pathways during exploration pipeline, the framework incorporates thermodynamic variables and environmental factors - including temperature, pressure, and solvent - to enable targeted, environment-induced sampling. Validation demonstrates that 71.7–91.8% of generated conformers are energetically favorable, exhibiting superior stability and diversity compared to simulated annealing with a ~ 1200-fold speedup. Critically, GenGLem captures pronounced environment-induced structural reorganization, maintaining high consistency with experimental observations. This study establishes a new framework bridging deep learning and molecular dynamics for rapid PES sampling, offering a transformative tool for advancing molecular property prediction and computational chemistry applications. Physical sciences/Chemistry/Cheminformatics Physical sciences/Chemistry/Chemical safety Full Text Additional Declarations There is NO Competing Interest. Supplementary Files supplementaryinformation.docx GenGLem: a generative framework for capturing chemical cliffs in energetic materials MLreportingsummary17721575601.pdf Reporting Summary Cite Share Download PDF Status: Under Review 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-8611356","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":595536185,"identity":"e89bfbfb-7937-4b39-8622-8a133d4bb20a","order_by":0,"name":"Huajie Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACPmYGNiDFxsPA3sAAYTAwGODVwgbXwnOYWC1gBAISyXBBAlrYecwe89TwyfBLvj/4uOAXnwwDe/M2CYaaO3gcxmNuzHOMjUdydjKz8cw+kAuPlUkwHHuGT4uZNA8bG4/B7WQ2ad4eoBaJHDMJxobDBLT8Y+Oxv3kYqkX+DRFaeNuAtkgws0nz/ADZwkNIC1uZ5FygFyTOJBsb8zaw8bDxpBVbJBzDrYWf//A2iTffjtnztx98+JjnD5DBfnjjjQ81uLVAwTEIxdh2DBJNCYQ0MDDUQOk/NXiVjYJRMApGwcgEAMZDPIZigEgkAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-1359-7200","institution":"Tongji University","correspondingAuthor":true,"prefix":"","firstName":"Huajie","middleName":"","lastName":"Liu","suffix":""},{"id":595536186,"identity":"87ca8cba-893e-4e7c-b5a0-ed0f5e740c9b","order_by":1,"name":"Wenjing Wu","email":"","orcid":"","institution":"Tongji university","correspondingAuthor":false,"prefix":"","firstName":"Wenjing","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2026-01-15 14:22:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8611356/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8611356/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103713464,"identity":"998a8b81-0239-4eee-bbae-dcfd601b37b4","added_by":"auto","created_at":"2026-03-02 04:45:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1248005,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptN.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8611356/v1_covered_4603054a-55d2-4cf9-bfb6-a5b81a0964ab.pdf"},{"id":103713463,"identity":"9ac7573d-ac7e-49ee-9743-2bf429c34882","added_by":"auto","created_at":"2026-03-02 04:45:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6488544,"visible":true,"origin":"","legend":"GenGLem: a generative framework for capturing chemical cliffs in energetic materials","description":"","filename":"supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8611356/v1/4304daac3e798c56da859773.docx"},{"id":103713462,"identity":"cab3c4df-d2a1-41b9-bb29-bb14812263a7","added_by":"auto","created_at":"2026-03-02 04:45:32","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":269087,"visible":true,"origin":"","legend":"Reporting Summary","description":"","filename":"MLreportingsummary17721575601.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8611356/v1/826b00abd9f59cca0c642e01.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"GenGLem: a generative framework for capturing chemical cliffs in energetic materials","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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-8611356/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8611356/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh-throughput prediction of explosive properties is currently bottlenecked by the prohibitive computational cost of potential energy surface (PES) sampling and the inability to account for complex environmental conditions. Here, we develop GenGLem, a dual-pathway generative framework featuring latent space exploration and microstructure guidance. GenGLem fundamentally transforms configurational sampling from traditional trajectory-based search into direct, end-to-end latent space generation. By integrating unconditional latent exploration and conditional diffusion pathways during exploration pipeline, the framework incorporates thermodynamic variables and environmental factors - including temperature, pressure, and solvent - to enable targeted, environment-induced sampling. Validation demonstrates that 71.7\u0026ndash;91.8% of generated conformers are energetically favorable, exhibiting superior stability and diversity compared to simulated annealing with a\u0026thinsp;~\u0026thinsp;1200-fold speedup. Critically, GenGLem captures pronounced environment-induced structural reorganization, maintaining high consistency with experimental observations. 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