Advanced Machine Learning QuantumMonte Carlo Methods for Neutron Matterusing Chiral Effective Field Theory

Advanced Machine Learning QuantumMonte Carlo Methods for Neutron Matterusing Chiral Effective Field Theory | 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 Advanced Machine Learning QuantumMonte Carlo Methods for Neutron Matterusing Chiral Effective Field Theory Hossein Sadeghi, Roshanak Ghorbani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6567870/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 We present a machine learning-enhanced Quantum Monte Carlo (QMC) approach for studying neutron matter using chiral effective field theory (EFT) interactions. Our method combines auxiliary field diffusion Monte Carlo (AFDMC)with neural network-optimized trial wave functions, achieving a 60% reduction invariance compared to traditional approaches. We performed the first N2LO chiral EFT calculations of neutron matter, including consistent three-nucleon (3N)forces within the AFDMC, with comprehensive uncertainty quantification. Our results bridge nuclear physics and astrophysics, providing precise connections between microscopic nuclear interactions and neutron-star observables. The neural network wave functions capture complex many-body correlations, resolving previous tensions with astrophysical constraints from LIGO/Virgo and NICER observations. This work establishes a new standard for the accuracy of quantum many-body calculations while offering insights into the neutron star equation of state. Physical sciences/Physics/Nuclear physics Physical sciences/Physics/Nuclear physics/Theoretical nuclear physics Quantum Monte Carlo Chiral Effective Field Theory Machine Learning Nuclear Equation of State Full Text Additional Declarations No competing interests reported. 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-6567870","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":453345322,"identity":"d230a666-269a-4bf3-b555-ec72040e11c7","order_by":0,"name":"Hossein Sadeghi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBACAxDBw8CQwMbeAGQdgIgeIE4LzwFStTBIJBBSCwXmDOwPP7ypqM3jk3z87MOPM4cZ+NsPMB6uwKPFsoHHWHLOmePFbNJpxjN7bhxmkDiTwHDwDD6H3X/DIM3bdiyxTTrBmIHnw2EGhhsMDAcb8Gk5wP74N+8/oBbJ458Z/wC1yBPWwmAmzdtQk9gmwWPMzAN0mAFhLTxmlnOOHUhs48kpZpY5k85jeCaxgaDDbrypqUuc3358M+ObY9ZycscPH/6ITwsUHIazgHHESIQGBoY6YhSNglEwCkbBSAUAMjRTiykjMkwAAAAASUVORK5CYII=","orcid":"","institution":"Arak University","correspondingAuthor":true,"prefix":"","firstName":"Hossein","middleName":"","lastName":"Sadeghi","suffix":""},{"id":453345323,"identity":"f40029da-44cd-41fe-9346-d91382d69904","order_by":1,"name":"Roshanak Ghorbani","email":"","orcid":"","institution":"Arak University","correspondingAuthor":false,"prefix":"","firstName":"Roshanak","middleName":"","lastName":"Ghorbani","suffix":""}],"badges":[],"createdAt":"2025-04-30 21:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6567870/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6567870/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83268460,"identity":"47e11030-e19f-4148-bafc-9cab85de65a3","added_by":"auto","created_at":"2025-05-22 07:02:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":621783,"visible":true,"origin":"","legend":"","description":"","filename":"paperfinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6567870/v1_covered_737ea488-539e-4ca8-a373-de740cdb7ea3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Advanced Machine Learning QuantumMonte Carlo Methods for Neutron Matterusing Chiral Effective Field Theory","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Quantum Monte Carlo, Chiral Effective Field Theory, Machine Learning, Nuclear Equation of State","lastPublishedDoi":"10.21203/rs.3.rs-6567870/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6567870/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"We present a machine learning-enhanced Quantum Monte Carlo (QMC) approach for studying neutron matter using chiral effective field theory (EFT) interactions. 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