Large Language Models for Material Science: A Systematic Review

Large Language Models for Material Science: A Systematic Review | 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 Large Language Models for Material Science: A Systematic Review Cecília Coelho, Oliver Niggemann This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9377879/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 Large language models (LLMs) are increasingly reshaping how text, code, and knowledge are processed, and likewise they are beginning to influence the material science field. This systematic review synthesises 102 recent peer-reviewed studies that apply LLMs to materials problems. We categorise methods over five dimensions: task-level use case (materials discovery, properties prediction, literature mining and knowledge extraction, dataset generation, and workflow automation); LLM architecture and interaction paradigm (encoder-only vs. decoder-only vs. multimodal, tool use and multi-agent systems); data modalities and materials domains; evaluation metrics and baselines; and reproducibility indicators, including data and code/model availability. From the surveyed studies, decoder-only GPT- and Llama-based models dominate literature mining, dataset generation, and workflow automation, while encoder-only BERT-based models are more used in property prediction and information extraction. We find encouraging performance in many task-specific settings, but also substantial fragmentation with heterogeneous datasets and metrics complicating cross-study comparison, and a significant amount of works have private data or unreleased code, limiting reproducibility. We conclude by outlining open challenges and future directions, such as the need for shared benchmarks ranging multiple materials domains and modalities, tighter integration of LLMs with physics-based simulations and experiments, and more systematic development of open, domain-tuned LLMs and agentic frameworks. Large Language Models Material Science Material Discovery Property Prediction Inverse Design Literature Mining Knowledge Extraction Dataset Generation Automation Agentic Systems Scientific-Machine Learning 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-9377879","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621242233,"identity":"b38ebfaa-9710-4236-98c4-a9ed57689ac7","order_by":0,"name":"Cecília Coelho","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIie3QMQrCQBBA0ZGANiu2AcFcYcVSyVl2EbQRDdgELAwI5grpcgWPEBjQJmgr2ChpLVJZWTijaCGsWlrsbzLNY3YCYLP9adlR3r8VBOgpGqoOgPOZqAcBIoPfCCh4EfxOGvHilKnA5yHDINyNvURViwC6LRNx87Wkh/V5UJjkh6ncq1ongWHHRKQ74lscHiTWlwe9cieXpgDUkYl4ZyZzIpOSyFan9DAmcyNxBRO8ryOS6Wj/IMog6IQB37IRfBSKvK9X+ZGIHLZNWxoxFmV5nbX41xUi9HUa85aw65m2PBPvD/4GbDabzfapG4i8VIrU5PBoAAAAAElFTkSuQmCC","orcid":"","institution":"Helmut Schmidt University","correspondingAuthor":true,"prefix":"","firstName":"Cecília","middleName":"","lastName":"Coelho","suffix":""},{"id":621242234,"identity":"a94115b2-8034-465a-a5e8-99d2b9d52b26","order_by":1,"name":"Oliver Niggemann","email":"","orcid":"","institution":"Helmut Schmidt University","correspondingAuthor":false,"prefix":"","firstName":"Oliver","middleName":"","lastName":"Niggemann","suffix":""}],"badges":[],"createdAt":"2026-04-10 09:56:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9377879/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9377879/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106960196,"identity":"221faaee-63eb-4168-b025-9268caa00bda","added_by":"auto","created_at":"2026-04-15 09:19:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":719944,"visible":true,"origin":"","legend":"","description":"","filename":"LLMsforMaterialSciencesurvey.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9377879/v1_covered_3b4c13b8-dca2-4564-84b8-e8ee3cad3d74.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Large Language Models for Material Science: A Systematic Review","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":"Large Language Models, Material Science, Material Discovery, Property Prediction, Inverse Design, Literature Mining, Knowledge Extraction, Dataset Generation, Automation, Agentic Systems, Scientific-Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-9377879/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9377879/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Large language models (LLMs) are increasingly reshaping how text, code, and knowledge are processed, and likewise they are beginning to influence the material science field. 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