Emergent Molecular Complexity in Prebiotic Chemistry Simulations: A Physics-Based Approach

Emergent Molecular Complexity in Prebiotic Chemistry Simulations: A Physics-Based Approach | 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 Emergent Molecular Complexity in Prebiotic Chemistry Simulations: A Physics-Based Approach Michał Klawikowski This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8279774/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 Background: The emergence of complex organic molecules from simple precursors remains a fundamental question in the origin of life. While experimental prebiotic chemistry has identified key reaction pathways, computational approaches capable of discovering novel reactions and autocatalytic networks are limited by either excessive computational cost (ab initio methods) or oversimplification (abstract reaction networks). Methods: We present a physics-based particle simulation framework that models prebiotic chemistry through continuous molecular dynamics with validated thermodynamic properties. The simulation employs literature-derived bond parameters, adaptive timestep integration, and real-time chemical novelty detection. We conducted 30 independent simulations across three prebiotic scenarios: Miller-Urey reducing atmosphere, alkaline hydrothermal vents, and formamide-rich environments, each running for 500,000 simulation steps (~140 hours of simulated time). Results: Our simulations generated 2,315 unique molecular species across all scenarios, with significant diversity differences between conditions. We detected 769,315 autocatalytic cycles, including both direct autocatalysis (1,199 instances) and indirect hypercycles (732,021 instances). The Miller-Urey scenario showed the highest autocatalytic cycle frequency (20,555 ± 84,750 cycles/run), followed by hydrothermal vents (11,403 ± 47,014) and formamide environments (10,782 ± 44,457). Network analysis revealed distinct hub molecules serving as key intermediates in each scenario. Amplification factors ranged from 1.11 to 6.0 (median 1.43), demonstrating significant autocatalytic enhancement of molecular abundances. Significance: This work demonstrates that physics-based simulations can discover emergent chemical complexity without pre-defined reaction rules, providing testable predictions for experimental validation. The detection of scenario-specific autocatalytic networks suggests multiple plausible pathways toward chemical evolution, supporting the idea of inevitable emergence of complexity in diverse prebiotic conditions. prebiotic chemistry origin of life molecular dynamics autocatalysis emergent complexity Full Text Additional Declarations No competing interests reported. Supplementary Files tableS1parameters.tex tableS2networkmetrics.tex 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. 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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-8279774","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":557487380,"identity":"5e9e7710-c5d4-483f-800c-149891cf0a5f","order_by":0,"name":"Michał Klawikowski","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Michał","middleName":"","lastName":"Klawikowski","suffix":""}],"badges":[],"createdAt":"2025-12-04 13:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8279774/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8279774/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97880158,"identity":"3ae3a784-6f4e-4ab1-af08-8d21491559b5","added_by":"auto","created_at":"2025-12-10 12:13:03","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4500,"visible":true,"origin":"","legend":"","description":"","filename":"5a16c8057ec94f8292336bb6bb857971.json","url":"https://assets-eu.researchsquare.com/files/rs-8279774/v1/c5ae2e29679372492ae9a976.json"},{"id":99788770,"identity":"45ca2818-495c-40fc-a998-180e33716a6a","added_by":"auto","created_at":"2026-01-08 12:47:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4639756,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptsubmission2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8279774/v1_covered_a6fbc9c8-b224-420d-b2ec-c4a2d69fb2be.pdf"},{"id":97880156,"identity":"1a6b95c5-3f72-43fe-924a-0c9f11840d5c","added_by":"auto","created_at":"2025-12-10 12:13:03","extension":"tex","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3178,"visible":true,"origin":"","legend":"","description":"","filename":"tableS1parameters.tex","url":"https://assets-eu.researchsquare.com/files/rs-8279774/v1/ae5c5787b715f0c73915abd0.tex"},{"id":97880157,"identity":"9e3021af-6ac7-4419-8289-ffb93a6dd64a","added_by":"auto","created_at":"2025-12-10 12:13:03","extension":"tex","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2368,"visible":true,"origin":"","legend":"","description":"","filename":"tableS2networkmetrics.tex","url":"https://assets-eu.researchsquare.com/files/rs-8279774/v1/1a5de763a0620523668d99a5.tex"}],"financialInterests":"No competing interests reported.","formattedTitle":"Emergent Molecular Complexity in Prebiotic Chemistry Simulations: A Physics-Based Approach","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"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":"prebiotic chemistry, origin of life, molecular dynamics, autocatalysis, emergent complexity","lastPublishedDoi":"10.21203/rs.3.rs-8279774/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8279774/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: The emergence of complex organic molecules from simple precursors remains a fundamental question in the origin of life. While experimental prebiotic chemistry has identified key reaction pathways, computational approaches capable of discovering novel reactions and autocatalytic networks are limited by either excessive computational cost (ab initio methods) or oversimplification (abstract reaction networks).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: We present a physics-based particle simulation framework that models prebiotic chemistry through continuous molecular dynamics with validated thermodynamic properties. The simulation employs literature-derived bond parameters, adaptive timestep integration, and real-time chemical novelty detection. We conducted 30 independent simulations across three prebiotic scenarios: Miller-Urey reducing atmosphere, alkaline hydrothermal vents, and formamide-rich environments, each running for 500,000 simulation steps (~140 hours of simulated time).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Our simulations generated 2,315 unique molecular species across all scenarios, with significant diversity differences between conditions. We detected 769,315 autocatalytic cycles, including both direct autocatalysis (1,199 instances) and indirect hypercycles (732,021 instances). The Miller-Urey scenario showed the highest autocatalytic cycle frequency (20,555 ± 84,750 cycles/run), followed by hydrothermal vents (11,403 ± 47,014) and formamide environments (10,782 ± 44,457). Network analysis revealed distinct hub molecules serving as key intermediates in each scenario. 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