{"paper_id":"121b12a5-3276-470b-99ed-c5ccafc3c4cd","body_text":"KAN-Enhanced Contrastive Learning Accelerating Crystal Structure Identification from XRD Patterns | 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 KAN-Enhanced Contrastive Learning Accelerating Crystal Structure Identification from XRD Patterns Chenlei Xu, Tianhao Su, Jie Xiong, Yue Wu, Shuya Dong, Tian Jiang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7938426/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Feb, 2026 Read the published version in npj Computational Materials → Version 1 posted 9 You are reading this latest preprint version Abstract Accurate determination of crystal structures is central to materials science, underpinning the understanding of composition–structure–property relationships and the discovery of new materials. Powder X-ray diffraction (XRD) is a key technique in this pursuit due to its versatility and reliability, yet current analysis pipelines still rely heavily on expert knowledge and slow iterative fitting, limiting their scalability in high-throughput and autonomous settings. Here we introduce a physics-guided contrastive learning framework, XRD–Crystal Contrastive Pretraining (XCCP), which aligns powder diffraction patterns with candidate crystal structures in a shared embedding space to enable efficient structure retrieval and symmetry recognition. The XRD encoder employs a dual-expert design with a Kolmogorov–Arnold Network projection head: one branch emphasizes low-angle reflections reflecting long-range order, while the other captures dense high-angle peaks shaped by symmetry. Coupled with a crystal graph encoder, contrastive pretraining yields physically grounded representations. XCCP demonstrates strong performance across tasks, with structure retrieval reaching 88.98% and space group identification attains 93.39% accuracy. The framework further generalizes to compositionally similar multi-principal element alloys and demonstrates zero-shot transfer to experimental patterns. Together, these results establish XCCP as a robust, interpretable, and scalable approach that offers a new paradigm for PXRD analysis, facilitating high-throughput screening, rapid structural validation, and integration into autonomous laboratories. Physical sciences/Engineering Physical sciences/Materials science Physical sciences/Mathematics and computing Physical sciences/Physics Powder X-ray diffraction Crystal structure determination Machine learning High-throughput materials screening Autonomous laboratories Full Text Additional Declarations No competing interests reported. Supplementary Files SM.docx Cite Share Download PDF Status: Published Journal Publication published 28 Feb, 2026 Read the published version in npj Computational Materials → Version 1 posted Editorial decision: Revision requested 05 Dec, 2025 Reviews received at journal 25 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviews received at journal 12 Nov, 2025 Reviewers agreed at journal 09 Nov, 2025 Reviewers invited by journal 06 Nov, 2025 Editor assigned by journal 06 Nov, 2025 Submission checks completed at journal 03 Nov, 2025 First submitted to journal 24 Oct, 2025 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. 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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-7938426\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":541029972,\"identity\":\"99f89e7e-d28e-4f2a-a27c-9f3acd071ea6\",\"order_by\":0,\"name\":\"Chenlei Xu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Chenlei\",\"middleName\":\"\",\"lastName\":\"Xu\",\"suffix\":\"\"},{\"id\":541029973,\"identity\":\"4497ca08-d17f-4f44-a239-47a6b1dee631\",\"order_by\":1,\"name\":\"Tianhao 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Materials\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"NPJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Powder X-ray diffraction, Crystal structure determination, Machine learning, High-throughput materials screening, Autonomous laboratories\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7938426/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7938426/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eAccurate determination of crystal structures is central to materials science, underpinning the understanding of composition\\u0026ndash;structure\\u0026ndash;property relationships and the discovery of new materials. Powder X-ray diffraction (XRD) is a key technique in this pursuit due to its versatility and reliability, yet current analysis pipelines still rely heavily on expert knowledge and slow iterative fitting, limiting their scalability in high-throughput and autonomous settings. Here we introduce a physics-guided contrastive learning framework, XRD\\u0026ndash;Crystal Contrastive Pretraining (XCCP), which aligns powder diffraction patterns with candidate crystal structures in a shared embedding space to enable efficient structure retrieval and symmetry recognition. The XRD encoder employs a dual-expert design with a Kolmogorov\\u0026ndash;Arnold Network projection head: one branch emphasizes low-angle reflections reflecting long-range order, while the other captures dense high-angle peaks shaped by symmetry. Coupled with a crystal graph encoder, contrastive pretraining yields physically grounded representations. XCCP demonstrates strong performance across tasks, with structure retrieval reaching 88.98% and space group identification attains 93.39% accuracy. The framework further generalizes to compositionally similar multi-principal element alloys and demonstrates zero-shot transfer to experimental patterns. Together, these results establish XCCP as a robust, interpretable, and scalable approach that offers a new paradigm for PXRD analysis, facilitating high-throughput screening, rapid structural validation, and integration into autonomous laboratories.\\u003c/p\\u003e\",\"manuscriptTitle\":\"KAN-Enhanced Contrastive Learning Accelerating Crystal Structure Identification from XRD Patterns\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-07 08:46:46\",\"doi\":\"10.21203/rs.3.rs-7938426/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-12-05T14:35:35+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-11-25T22:23:23+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"185418563181244882600339507057253651533\",\"date\":\"2025-11-13T16:00:50+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-11-12T05:43:08+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"49031988012727821689484952028302562597\",\"date\":\"2025-11-09T20:24:53+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-11-06T13:43:46+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-11-06T12:49:28+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-11-03T09:37:48+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"npj Computational Materials\",\"date\":\"2025-10-24T08:12:14+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"npj-computational-materials\",\"isNatureJournal\":false,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"npjcompumats\",\"sideBox\":\"Learn more about [npj Computational Materials](http://www.nature.com/npjcompumats/)\",\"snPcode\":\"41524\",\"submissionUrl\":\"https://mts-npjcompumats.nature.com/\",\"title\":\"npj Computational Materials\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"NPJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"592876bc-cb67-4e89-a77e-ca816f59eca5\",\"owner\":[],\"postedDate\":\"November 7th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[{\"id\":57556232,\"name\":\"Physical sciences/Engineering\"},{\"id\":57556233,\"name\":\"Physical sciences/Materials science\"},{\"id\":57556234,\"name\":\"Physical sciences/Mathematics and computing\"},{\"id\":57556235,\"name\":\"Physical sciences/Physics\"}],\"tags\":[],\"updatedAt\":\"2026-03-02T16:02:54+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-7938426\",\"link\":\"https://doi.org/10.1038/s41524-026-02015-y\",\"journal\":{\"identity\":\"npj-computational-materials\",\"isVorOnly\":false,\"title\":\"npj Computational Materials\"},\"publishedOn\":\"2026-02-28 15:57:42\",\"publishedOnDateReadable\":\"February 28th, 2026\"},\"versionCreatedAt\":\"2025-11-07 08:46:46\",\"video\":\"\",\"vorDoi\":\"10.1038/s41524-026-02015-y\",\"vorDoiUrl\":\"https://doi.org/10.1038/s41524-026-02015-y\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7938426\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7938426\",\"identity\":\"rs-7938426\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}