{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T16:52:11Z","timestamp":1791132731951,"version":"4.1.0"},"reference-count":32,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T00:00:00Z","timestamp":1791072000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T00:00:00Z","timestamp":1791072000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS\u20102025\u201016070951"],"award-info":[{"award-number":["RS\u20102025\u201016070951"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS\u20102023\u201000222166"],"award-info":[{"award-number":["RS\u20102023\u201000222166"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"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                    Architected materials derive their high performance from geometry as much as from intrinsic material properties, yet most optimization frameworks search within predefined topology families, limiting the discovery of genuinely new morphologies. Here, we investigate whether superior architectures exist beyond the reach of these parameterized families, and demonstrate that they can be systematically discovered. We present a framework that searches directly in voxelated structural space. Cubic\u2010symmetric unit cells drawn from strut\u2010based, TPMS\u2010based, and hybrid families are encoded into a continuous latent representation, and surrogate\u2010guided multi\u2010objective search with an active learning loop repeatedly converts promising predictions into high\u2010fidelity validated data. Across nine rounds, the mean minimum Hamming distance from the initial dataset increases from 442 to 641 and the Pareto hypervolume rises from 0.106 to 0.113, confirming systematic expansion of the validated design domain. Computationally, the discovered architectures achieve 11\u201320% higher relative Young's modulus than initial designs at matched density. Fabricated via two\u2010photon polymerization and pyrolysis, the optimized carbon nano\u2010architectures outperform density\u2010matched counterparts by 18\u201323% in Young's modulus and 26\u201340% in strength, reaching specific stiffnesses of 17.2, 22.3, and 21.3\u00a0MPa m\n                    <jats:sup>3<\/jats:sup>\n                    kg\n                    <jats:sup>\u2212<\/jats:sup>\n                    <jats:sup>1<\/jats:sup>\n                    . These results establish that direct learning in structural space enables discovering mechanically efficient nano\u2010architectures beyond the initial parameterized families.\n                  <\/jats:p>","DOI":"10.1002\/advs.78140","type":"journal-article","created":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T16:02:21Z","timestamp":1791129741000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Specific Stiffness of Nano\u2010Architected Materials Through Generative AI and Active Learning"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4688-1611","authenticated-orcid":false,"given":"Jinwook","family":"Yeo","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering Korea Advanced Institute of Science and Technology (KAIST)  Daejeon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3105-4672","authenticated-orcid":false,"given":"Peter","family":"Serles","sequence":"additional","affiliation":[{"name":"Division of Engineering and Applied Science California Institute of Technology  Pasadena California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6524-2858","authenticated-orcid":false,"given":"Donggeun","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering Korea Advanced Institute of Science and Technology (KAIST)  Daejeon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9675-1508","authenticated-orcid":false,"given":"Julia R.","family":"Greer","sequence":"additional","affiliation":[{"name":"Division of Engineering and Applied Science California Institute of Technology  Pasadena California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9516-5809","authenticated-orcid":false,"given":"Seunghwa","family":"Ryu","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering Korea Advanced Institute of Science and Technology (KAIST)  Daejeon Republic of Korea"},{"name":"Department of AX Korea Advanced Institute of Science and Technology (KAIST)  Daejeon Republic of Korea"},{"name":"KAIST InnoCORE PRISM\u2010AI Center Korea Advanced Institute of Science and Technology (KAIST)  Daejeon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,4]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1252291"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010024\u201047089\u20108"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010020\u201015434\u20102"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1255908"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.adw6632"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/adma.202410651"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmecsci.2018.03.027"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/ma12132192"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2017.12.006"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1080\/15376494.2021.1948151"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/adfm.202526595"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s004190050248"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/nme.3072"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/adma.202302530"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/adma.202303481"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.matdes.2024.113523"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmech.2024.1417606"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256\u2010023\u201000762\u2010x"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2023.103764"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1080\/17452759.2025.2536560"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00158\u2010025\u201004231\u20104"},{"key":"e_1_2_10_23_1","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/978-3-031-24628-9_16","volume-title":"Machine 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