Efficient Rare Event Sampling with Unsupervised Normalising Flows

Efficient Rare Event Sampling with Unsupervised Normalising Flows | 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 Efficient Rare Event Sampling with Unsupervised Normalising Flows Ran Ni, Solomon Asghar, Qing-Xiang Pei, Giorgio Volpe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3887535/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Nov, 2024 Read the published version in Nature Machine Intelligence → Version 1 posted You are reading this latest preprint version Abstract From Physics and Biology to Seismology and Economics, the behaviour of countless systems is determined by impactful yet unlikely transitions between metastable states known as rare events, the study of which is essential for understanding and controlling these systems’ properties. Classical computational methods to sample rare events remain prohibitively inefficient and are bottlenecks for enhanced samplers requiring prior data. Here, we introduce a novel physics-informed machine learning framework, FlowRES, that uses unsupervised normalising flow neural networks to enhance Monte Carlo sampling of rare events by generating high-quality nonlocal Monte Carlo proposals. We validate FlowRES by sampling the transition path ensembles of equilibrium and non-equilibrium systems of Brownian particles exploring increasingly complex potentials. Beyond eliminating requirements for prior data, FlowRES features key advantages over established samplers: no collective variables need defining, efficiency remains constant even as events become increasingly rare, and systems with multiple routes between states can be straightforwardly simulated. Physical sciences/Physics/Statistical physics, thermodynamics and nonlinear dynamics Physical sciences/Mathematics and computing/Computational science Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SI.pdf Cite Share Download PDF Status: Published Journal Publication published 19 Nov, 2024 Read the published version in Nature Machine Intelligence → 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-3887535","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":275671321,"identity":"c0481ff6-bdcc-46f9-ad6e-77facd44d667","order_by":0,"name":"Ran Ni","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYJCCAx8gtAHxWg7OgKgmQQszD0lazBt4Dx623fEnsYG9eZsEw59awlpkDvAlHM49Y5DYwHOsTIKx7ThhLRIMPAaHc9uAWiRyzCQYG44RqcUSpEX+jRnQYcRqYQTbwgPUwlZDhBZmHoODvW3Gxm08acUWiW0HiNDC3mP84WebnGw/++GNNz78qSOshYEZSrOBiASGw0RoQQPE2DIKRsEoGAUjDQAA5R0yK+s3bmUAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9478-0674","institution":"Nanyang Technological University","correspondingAuthor":true,"prefix":"","firstName":"Ran","middleName":"","lastName":"Ni","suffix":""},{"id":275671322,"identity":"a85c66db-08a9-4729-8460-adf7932e6ea9","order_by":1,"name":"Solomon Asghar","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Solomon","middleName":"","lastName":"Asghar","suffix":""},{"id":275671323,"identity":"29c3507b-a38e-487d-b371-404679fee894","order_by":2,"name":"Qing-Xiang Pei","email":"","orcid":"","institution":"Institute of High Performance Computing","correspondingAuthor":false,"prefix":"","firstName":"Qing-Xiang","middleName":"","lastName":"Pei","suffix":""},{"id":275671324,"identity":"8ca3817d-d17f-4b7f-bd16-940f609b7e01","order_by":3,"name":"Giorgio Volpe","email":"","orcid":"https://orcid.org/0000-0001-9993-5348","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Giorgio","middleName":"","lastName":"Volpe","suffix":""}],"badges":[],"createdAt":"2024-01-22 09:32:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3887535/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3887535/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s42256-024-00918-3","type":"published","date":"2024-11-19T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69422613,"identity":"407c95f4-aa71-43f0-87a1-a79b67653522","added_by":"auto","created_at":"2024-11-20 08:07:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1557175,"visible":true,"origin":"","legend":"Article File","description":"","filename":"EfficientRareEventSamplingwithUnsupervisedNormalisingFlowsNatureMI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3887535/v1_covered_465b89f4-9a06-4986-9e81-32695041e2c1.pdf"},{"id":52042113,"identity":"2285c4b6-c70f-4ab9-b548-ae73862e6fe2","added_by":"auto","created_at":"2024-03-05 18:28:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3518740,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3887535/v1/67a512acbf1230e4776df8e8.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Efficient Rare Event Sampling with Unsupervised Normalising Flows","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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-3887535/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3887535/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"From Physics and Biology to Seismology and Economics, the behaviour of countless systems is determined by impactful yet unlikely transitions between metastable states known as rare events, the study of which is essential for understanding and controlling these systems’ properties. 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