Combinatorial Precursor Set Prediction for Inorganic Materials Synthesis via Graph-Conditioned Sequence Generation

Combinatorial Precursor Set Prediction for Inorganic Materials Synthesis via Graph-Conditioned Sequence Generation | 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 Combinatorial Precursor Set Prediction for Inorganic Materials Synthesis via Graph-Conditioned Sequence Generation Gourab Datta, Sarah Sharif, Yaser Banad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9183637/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 The realization of novel inorganic materials is fundamentally bottlenecked by the challenge of experimental synthesis planning. Recent data-driven approaches primarily frame precursor selection as a retrieval or ranking problem, relying on historical precedent to propose recipes for new targets. Here, we demonstrate that this precedent-driven paradigm is strictly constrained by a combinatorial ceiling. Evaluating N=20,103 materials reported post-2020 against the pre-2020 literature, we find that while 92.4% of individual precursor ingredients are familiar, the exact required combinations are entirely unseen in 46.8% of cases. Consequently, any extractive or template-based retrieval method faces a hard oracle upper bound of 53.2% exact-match accuracy. To overcome this barrier, we recast synthesis planning as generative reasoning under combinatorial constraints. We introduce a dual-modality sequence-to-sequence generator that dynamically constructs open-ended precursor sets rather than extracting them. By fusing elemental descriptors with 3D geometric embeddings, our approach explicitly resolves polymorph-specific constraints that composition alone underdetermines. On the strict time-split benchmark, our hybrid generative framework achieves a 32.5\% exact-match accuracy overall, more than doubling the performance of state-of-the-art retrieve-and-recombine baselines. Most notably, the model achieves a 21.9\% exact match on genuinely novel precursor combinations, a domain where the theoretical oracle limit for extractive retrieval is strictly zero. By shifting from historically constrained extraction to open-ended generative construction, this work overcomes the fundamental limits of analogy-based synthesis and enables the prospective planning of viable chemical routes for unprecedented inorganic solids. Physical sciences/Chemistry Physical sciences/Materials science Physical sciences/Mathematics and computing solid-state synthesis precursor selection chemical feasibility element compatibility materials informatics synthesis planning Full Text Additional Declarations No competing interests reported. Supplementary Files precursorsupplementary.pdf 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-9183637","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":614735488,"identity":"8e932824-0353-4123-b409-21aea05e4794","order_by":0,"name":"Gourab Datta","email":"","orcid":"","institution":"University of Oklahoma","correspondingAuthor":false,"prefix":"","firstName":"Gourab","middleName":"","lastName":"Datta","suffix":""},{"id":614735489,"identity":"699ce57f-c92a-41a4-b0de-849fcae44542","order_by":1,"name":"Sarah Sharif","email":"","orcid":"","institution":"University of Oklahoma","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Sharif","suffix":""},{"id":614735490,"identity":"8a54add7-e234-4331-81a5-3b3f272b07ef","order_by":2,"name":"Yaser Banad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAkklEQVRIiWNgGAWjYBACAxDxAcxMIEEL4wyStTDzkKTFnP3swc82FYcZ+NlzDIjTYtmTlyydc+Ywg2TPGyK1GBzIMZDObTvMYHCDWFsMzr8x/m0J1GJPvJYbOWbSjCBbJIj2y4w3ZpY9Z9J5JM48KyBOizl/jvGNHxXWcvztyRuI0wIDPKQpHwWjYBSMglGAHwAA19soTIrV70EAAAAASUVORK5CYII=","orcid":"","institution":"University of Oklahoma","correspondingAuthor":true,"prefix":"","firstName":"Yaser","middleName":"","lastName":"Banad","suffix":""}],"badges":[],"createdAt":"2026-03-21 06:24:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9183637/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9183637/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108088287,"identity":"bb3184fc-711f-4c29-858f-0328f6c44c32","added_by":"auto","created_at":"2026-04-29 08:56:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1132794,"visible":true,"origin":"","legend":"","description":"","filename":"CombinatorialPrecursorSetPredictionforInorganicMaterialsSynthesisviaGraphBasedAttentionMechanisms.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9183637/v1_covered_02dfdc92-b9bd-4609-8446-5bc10220a126.pdf"},{"id":105882760,"identity":"91abbfa4-f0b1-472c-b276-4a9a9a0b8845","added_by":"auto","created_at":"2026-04-01 06:58:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":325785,"visible":true,"origin":"","legend":"","description":"","filename":"precursorsupplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9183637/v1/91c04b324ff0dab50ae7b820.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combinatorial Precursor Set Prediction for Inorganic Materials Synthesis via Graph-Conditioned Sequence Generation","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":"solid-state synthesis, precursor selection, chemical feasibility, element compatibility, materials informatics, synthesis planning","lastPublishedDoi":"10.21203/rs.3.rs-9183637/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9183637/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe realization of novel inorganic materials is fundamentally bottlenecked by the challenge of experimental synthesis planning. Recent data-driven approaches primarily frame precursor selection as a retrieval or ranking problem, relying on historical precedent to propose recipes for new targets. Here, we demonstrate that this precedent-driven paradigm is strictly constrained by a combinatorial ceiling. Evaluating N=20,103 materials reported post-2020 against the pre-2020 literature, we find that while 92.4% of individual precursor ingredients are familiar, the exact required combinations are entirely unseen in 46.8% of cases. Consequently, any extractive or template-based retrieval method faces a hard oracle upper bound of 53.2% exact-match accuracy. To overcome this barrier, we recast synthesis planning as generative reasoning under combinatorial constraints. We introduce a dual-modality sequence-to-sequence generator that dynamically constructs open-ended precursor sets rather than extracting them. 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