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Consequently, the limited availability of complete experimental data impedes a deeper understanding of the underlying materials\u2019 mechanisms. In this work, we introduce a multiagent artificial intelligence framework that autonomously extracts information from PDF-formatted literature, enabling accurate retrieval of molecular structures, properties, detailed measurement conditions, and OLED device architectures. This is achieved by decoupling the complex task of data extraction from PDFs and integrating easily accessible, multiple specialized AI agents. Using this framework, we curated and publicly released data from over 1600 open-access publications, covering most known thermally activated delayed fluorescence (TADF) materials. When applied to this real-world data set, the trained machine learning (ML) models in our framework demonstrated high accuracy in predicting six key properties essential for materials screening. We further investigated the influence of additional experimental conditions on the predictive performance of the machine learning models. Our results demonstrate that lowering the barrier to acquiring OLED-related data using our framework can significantly enhance the prediction accuracy of ML models for emitter screening, accelerating AI-driven material discovery in this field.<\/jats:p>","DOI":"10.1021\/acsomega.6c03486","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T13:10:42Z","timestamp":1785849042000},"page":"46279-46289","source":"Crossref","is-referenced-by-count":0,"title":["Multiagent Data\nMining for Enhanced Machine Learning\nPrediction of Delayed Fluorescence Materials"],"prefix":"10.1021","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4171-4314","authenticated-orcid":true,"given":"Zhaoming","family":"He","sequence":"first","affiliation":[{"name":"South China University of Technology , , 381 Wushan Road , Tianhe District, , ,","place":["Guangzhou, Guangdong, China, 510640"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0254-8550","authenticated-orcid":true,"given":"Heming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Jihua Laboratory , 28 Huandao South Road , , ,","place":["Foshan, Guangdong Province, P. R. China, 528200"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6936-5081","authenticated-orcid":true,"given":"Yue","family":"Wang","sequence":"additional","affiliation":[{"name":"Jilin University , , 2699 Qianjin Avenue , ,","place":["Changchun, P. R. China, 130012"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanli","family":"Yang","sequence":"additional","affiliation":[{"name":"Jihua Laboratory , 28 Huandao South Road , , ,","place":["Foshan, Guangdong Province, P. R. China, 528200"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0393-0057","authenticated-orcid":true,"given":"Jiangshan","family":"Chen","sequence":"additional","affiliation":[{"name":"South China University of Technology , , 381 Wushan Road , Tianhe District, , ,","place":["Guangzhou, Guangdong, China, 510640"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2017-3668","authenticated-orcid":true,"given":"Hai","family":"Bi","sequence":"additional","affiliation":[{"name":"Jihua Laboratory , 28 Huandao South Road , , ,","place":["Foshan, Guangdong Province, P. R. China, 528200"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"2026081110192008700_cit1","doi-asserted-by":"publisher","first-page":"1120","DOI":"10.1038\/nmat4717","article-title":"Design of efficient molecular organic light-emitting\ndiodes by a high-throughput virtual screening and experimental approach","volume":"15","author":"Go\u0301mez-Bombarelli","year":"2016","journal-title":"Nat. Mater."},{"key":"2026081110192008700_cit2","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1038\/s41524-022-00834-3","article-title":"Deep learning for development of\norganic optoelectronic devices: efficient prescreening of hosts and\nemitters in deep-blue fluorescent OLEDs","volume":"8","author":"Jeong","year":"2022","journal-title":"Npj\nComput. Mater."},{"issue":"1","key":"2026081110192008700_cit3","doi-asserted-by":"publisher","first-page":"4099","DOI":"10.1038\/s41467-023-39868-6","article-title":"polyBERT:\nA chemical language model to enable fully machine-driven ultrafast\npolymer informatics","volume":"14","author":"Kuenneth","year":"2023","journal-title":"Nat. Commun."},{"key":"2026081110192008700_cit4","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1038\/s41586-020-2442-2","article-title":"A mobile robotic chemist","volume":"583","author":"Burger","year":"2020","journal-title":"Nature"},{"key":"2026081110192008700_cit5","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1038\/s41586-023-06734-w","article-title":"An autonomous laboratory\nfor the accelerated synthesis of inorganic materials","volume":"624","author":"Szymanski","year":"2023","journal-title":"Nature"},{"issue":"24","key":"2026081110192008700_cit6","doi-asserted-by":"publisher","first-page":"241722","DOI":"10.1063\/1.5019779","article-title":"SchNet \u2013 A deep learning architecture\nfor molecules\nand materials","volume":"148","author":"Schu\u0308tt","year":"2018","journal-title":"J. Chem. 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Learn."},{"key":"2026081110192008700_cit36","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1038\/s41377-024-01713-w","article-title":"Frontier molecular\norbital weighted model based networks for revealing organic delayed\nfluorescence efficiency","volume":"14","author":"He","year":"2025","journal-title":"Light: Sci. Appl."},{"issue":"1","key":"2026081110192008700_cit37","doi-asserted-by":"publisher","first-page":"2787","DOI":"10.1038\/s41467-023-38347-2","article-title":"A general\nmodel\nto predict small molecule substrates of enzymes based on machine and\ndeep learning","volume":"14","author":"Kroll","year":"2023","journal-title":"Nat. Commun."},{"key":"2026081110192008700_cit38","doi-asserted-by":"publisher","first-page":"1925","DOI":"10.1021\/acs.jcim.2c01480","article-title":"MolScribe:\nRobust Molecular Structure Recognition with Image-to-Graph Generation","volume":"63","author":"Qian","year":"2023","journal-title":"J. Chem. Inf. 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Model."},{"key":"2026081110192008700_cit41","doi-asserted-by":"crossref","first-page":"3393","DOI":"10.1109\/ICASSP43922.2022.9746088","article-title":"Image-to-Graph Transformers for Chemical\nStructure Recognition","volume-title":"ICASSP 2022 - 2022\nIEEE International Conference on Acoustics, Speech and Signal Processing\n(ICASSP)","author":"Yoo","year":"2022"},{"key":"2026081110192008700_cit42","unstructured":"Landrum, G.\n          \n          RDKit: Open-source\ncheminformatics software; 2016. RDKit."},{"key":"2026081110192008700_cit43","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","article-title":"Extended-Connectivity\nFingerprints","volume":"50","author":"Rogers","year":"2010","journal-title":"J. Chem.\nInf. 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