{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T21:26:49Z","timestamp":1791322009596,"version":"4.3.1"},"reference-count":80,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T00:00:00Z","timestamp":1790467200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T00:00:00Z","timestamp":1790467200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Science"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    In this work, we describe the successful application of an AI\u2010driven inverse design protocol to identify singlet fission (SF) candidates within the acene family, exploring a chemical space of \u224810\n                    <jats:sup>19<\/jats:sup>\n                    molecules. Substituent effects are encoded via Hammett \u03c3 constants, providing a chemically interpretable and generalizable descriptor for unseen functional groups. A Gated Recurrent Unit (GRU)\u2010based Recurrent Neural Network (RNN) model is used to accurately predict excited singlet (S\n                    <jats:sub>1<\/jats:sub>\n                    ) and triplet (T\n                    <jats:sub>1<\/jats:sub>\n                    ) states energies. The model is coupled with optimization algorithms, including Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to efficiently navigate chemical space, optimize S\n                    <jats:sub>1<\/jats:sub>\n                    and T\n                    <jats:sub>1<\/jats:sub>\n                    energies, and reveal recurring substituent patterns. This approach not only identifies well\u2010known tetracene and pentacene candidates but also uncovers viable benzene, naphthalene, and anthracene derivatives that satisfy SF criteria, which are typically inaccessible via intuition. The methodology, accessible at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/alba.ugr.es\/acene\/\">https:\/\/alba.ugr.es\/acene\/<\/jats:ext-link>\n                    , establishes a versatile and interpretable platform for rational molecular design, enabling the exploration of large chemical spaces and the discovery of compounds with tailored excited\u2010state properties.\n                  <\/jats:p>","DOI":"10.1002\/advs.76828","type":"journal-article","created":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T18:00:31Z","timestamp":1790532031000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence\u2010Driven Inverse Design of Singlet Fission Candidates in the Acene family"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4548-4868","authenticated-orcid":false,"given":"Rafael G.","family":"Uceda","sequence":"first","affiliation":[{"name":"Departamento de Qu\u00edmica Org\u00e1nica, Facultad De Ciencias Universidad De Granada (UGR)  Granada Spain"},{"name":"Unidad de Excelencia de Qu\u00edmica Aplicada a la Biomedicina y Medioambiente (UEQ) Universidad De Granada  Granada Spain"},{"name":"C\u00e1tedra de Energ\u00eda y Tecnolog\u00edas Cu\u00e1nticas Universidad De Granada  Granada Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5623-4039","authenticated-orcid":false,"given":"Boris","family":"P\u00e9rez\u2010Ca\u00f1edo","sequence":"additional","affiliation":[{"name":"Departamento de Qu\u00edmica Org\u00e1nica, Facultad De Ciencias Universidad De Granada (UGR)  Granada Spain"},{"name":"Unidad de Excelencia de Qu\u00edmica Aplicada a la Biomedicina y Medioambiente (UEQ) Universidad De Granada  Granada Spain"},{"name":"C\u00e1tedra de Energ\u00eda y Tecnolog\u00edas Cu\u00e1nticas Universidad De Granada  Granada Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0424-3574","authenticated-orcid":false,"given":"Sandra","family":"M\u00edguez\u2010Lago","sequence":"additional","affiliation":[{"name":"Departamento de Qu\u00edmica Org\u00e1nica, Facultad De Ciencias Universidad De Granada (UGR)  Granada Spain"},{"name":"Unidad de Excelencia de Qu\u00edmica Aplicada a la Biomedicina y Medioambiente (UEQ) Universidad De Granada  Granada Spain"},{"name":"C\u00e1tedra de Energ\u00eda y Tecnolog\u00edas Cu\u00e1nticas Universidad De Granada  Granada Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0676-5210","authenticated-orcid":false,"given":"Carlos M.","family":"Cruz","sequence":"additional","affiliation":[{"name":"Departamento de Qu\u00edmica Org\u00e1nica, Facultad De Ciencias Universidad De Granada (UGR)  Granada Spain"},{"name":"Unidad de Excelencia de Qu\u00edmica Aplicada a la Biomedicina y Medioambiente (UEQ) Universidad De Granada  Granada Spain"},{"name":"C\u00e1tedra de Energ\u00eda y Tecnolog\u00edas Cu\u00e1nticas Universidad De Granada  Granada Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joaqu\u00edn J.","family":"Torres","sequence":"additional","affiliation":[{"name":"C\u00e1tedra de Energ\u00eda y Tecnolog\u00edas Cu\u00e1nticas Universidad De Granada  Granada Spain"},{"name":"Instituto \u2018Carlos I\u2019 De F\u00edsica Te\u00f3rica y Computacional. 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