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In this study, we develop an interpretable AI framework for frontier orbital prediction using QM9 quantum\u2010chemical dataset and validate it across conventional and deep learning models. A direct message passing neural network delivers the best performance for frontier orbital prediction, while interpretability analyses consistently recover chemically meaningful substructures linked to orbital modulation. Extension of the optimized DMPNN model to 908\u00a0545\u00a0821 molecules in GDB\u201313 database enables AI\u2010guided ultra large\u2010scale donor screening framework development, referencing frontier orbital alignment to a representative ITIC acceptor for solar cells. This identifies only 37 visible region candidates, revealing the rarity of simultaneously satisfying donor\u2010like level alignment and narrow optical gaps. Scaffold\u2010resolved analysis further unravels diamino\u2010triketone class as a privileged donor building\u2010unit motif, combining small positive frontier orbital offsets with narrow gaps and favorable energy alignment. These show that quantum\u2010trained AI can unify predictive accuracy, chemical interpretability, and billion\u2010scale screening, providing a practical route for inverse design of organic optoelectronic molecules.<\/jats:p>","DOI":"10.1002\/advs.77414","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T15:43:45Z","timestamp":1787586225000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantum\u2010Trained AI Enables Inverse Design of Organic Frontier Orbitals at Billion\u2010Scale"],"prefix":"10.1002","author":[{"given":"Yeongnam","family":"Ko","sequence":"first","affiliation":[{"name":"Computational Materials Design Laboratory School of Chemical Biomolecular and Energy Engineering Konkuk University  Seoul The Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Se Jin","family":"Kim","sequence":"additional","affiliation":[{"name":"Computational Materials Design Laboratory School of Chemical Biomolecular and Energy Engineering Konkuk University  Seoul The Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9359-9811","authenticated-orcid":false,"given":"Ki Chul","family":"Kim","sequence":"additional","affiliation":[{"name":"Computational Materials Design Laboratory School of Chemical Biomolecular and Energy Engineering Konkuk University  Seoul The Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41578\u2010023\u201000618\u20101"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1039\/D3EE00908D"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/smll.202504603"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/adma.202311155"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/adfm.202105285"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1021\/jacs.5b13279"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ensm.2019.01.017"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41578\u2010019\u20100137\u20109"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmat5063"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.joule.2017.09.020"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/adma.200501717"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/natrevmats.2018.3"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1039\/C6EE02641A"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ensm.2020.04.009"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ensm.2019.08.014"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/anie.202409814"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/adfm.202418223"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202405303"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ensm.2025.104275"},{"key":"e_1_2_8_21_1","unstructured":"A.Nigam P.Friederich M.Krenn andA.Aspuru\u2010Guzik \u201cAugmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space \u201d preprint bioRxiv September 25 2019 https:\/\/doi.org\/10.48550\/arXiv.1909.11655."},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ensm.2020.12.002"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1039\/D0SC00554A"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010018\u20100337\u20102"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41524\u2010020\u201000388\u20102"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci300415d"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2014.22"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1039\/C7SC02664A"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13321\u2010020\u201000460\u20105"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1002\/wcms.1603"},{"key":"e_1_2_8_31_1","first-page":"4765","article-title":"A Unified Approach to Interpreting Model Predictions","volume":"30","author":"Lundberg S. 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