{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:53:52Z","timestamp":1780394032807,"version":"3.54.1"},"reference-count":55,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T00:00:00Z","timestamp":1696896000000},"content-version":"am","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T00:00:00Z","timestamp":1696896000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1940099"],"award-info":[{"award-number":["1940099"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1905775"],"award-info":[{"award-number":["1905775"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Intelligent Systems"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:p>\nTwo\u2010dimensional (2D) materials offer great potential in various fields like superconductivity, quantum systems, and topological materials. However, designing them systematically remains challenging due to the limited pool of fewer than 100 experimentally synthesized 2D materials. Recent advancements in deep learning, data mining, and density functional theory (DFT) calculations have paved the way for exploring new 2D material candidates. Herein, a generative material design pipeline known as the material transformer generator (MTG) is proposed. MTG leverages two distinct 2D material composition generators, both trained using self\u2010learning neural language models rooted in transformers, with and without transfer learning. These models generate numerous potential 2D compositions, which are plugged into established templates for known 2D materials to predict their crystal structures. To ensure stability, DFT computations assess their thermodynamic stability based on energy\u2010above\u2010hull and formation energy metrics. MTG has found four new DFT\u2010validated stable 2D materials: NiCl<jats:sub>4<\/jats:sub>, IrSBr, CuBr<jats:sub>3<\/jats:sub>, and CoBrCl, all with zero energy\u2010above\u2010hull values that indicate thermodynamic stability. Additionally, GaBrO and NbBrCl<jats:sub>3<\/jats:sub> are found with energy\u2010above\u2010hull values below 0.05\u2009eV. CuBr<jats:sub>3<\/jats:sub> and GaBrO exhibit dynamic stability, confirmed by phonon dispersion analysis. In summary, the MTG pipeline shows significant potential for discovering new 2D and functional materials.<\/jats:p>","DOI":"10.1002\/aisy.202300141","type":"journal-article","created":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T00:40:46Z","timestamp":1697071246000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Discovery of 2D Materials using Transformer Network\u2010Based Generative Design"],"prefix":"10.1002","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1467-6725","authenticated-orcid":false,"given":"Rongzhi","family":"Dong","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering University of South Carolina  Columbia SC 29201 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqi","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering University of South Carolina  Columbia SC 29201 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edirisuriya M. D.","family":"Siriwardane","sequence":"additional","affiliation":[{"name":"Department of Physics University of Colombo  Colombo 00300 Sri Lanka"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8725-6660","authenticated-orcid":false,"given":"Jianjun","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering University of South Carolina  Columbia SC 29201 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,10,10]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/adma.201904302"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1021\/accountsmr.1c00246"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41524-022-00923-3"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41524-020-00375-7"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1088\/2053-1583\/ac1059"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41565-017-0035-5"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/adma.202106970"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nanoms.2021.05.002"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1088\/2053-1583\/aaf836"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cobme.2019.08.016"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1102896"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature26160"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/natrevmats.2017.33"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/smm2.1031"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpclett.7b00222"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/nnano.2012.193"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/nnano.2015.340"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/natrevmats.2016.98"},{"key":"e_1_2_9_20_1","unstructured":"T.Xie X.Fu O.-E.Ganea R.Barzilay T.Jaakkola arXiv preprint arXiv:2110.06197 2021."},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevMaterials.3.034003"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1088\/2053-1583\/aacfc1"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-019-0097-3"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/ac9bca"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.nanolett.1c03841"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.4769731"},{"key":"e_1_2_9_27_1","first-page":"5998","volume":"30","author":"Vaswani A.","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_2_9_28_1","unstructured":"T.Shen V.Quach R.Barzilay T.Jaakkola inProc. of the 2020 Conf. on Empirical Methods in Natural Language Processing Association for Computational Linguistics November2020."},{"key":"e_1_2_9_29_1","unstructured":"L.Wei Q.Li Y.Song S.Stefanov E.Siriwardane F.Chen J.Hu arXiv preprint arXiv:2204.11953 2022."},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.commatsci.2017.01.017"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-648X\/aa63cd"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevMaterials.5.105003"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemmater.0c03381"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.commatsci.2012.10.028"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000019"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1039\/C9RA07755C"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.1002\/adma.202102507"},{"key":"e_1_2_9_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.commatsci.2022.111496"},{"key":"e_1_2_9_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mattod.2021.08.012"},{"key":"e_1_2_9_40_1","doi-asserted-by":"publisher","DOI":"10.1038\/s43588-022-00349-3"},{"key":"e_1_2_9_41_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.47.558"},{"key":"e_1_2_9_42_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.49.14251"},{"key":"e_1_2_9_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/0927-0256(96)00008-0"},{"key":"e_1_2_9_44_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.54.11169"},{"key":"e_1_2_9_45_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.50.17953"},{"key":"e_1_2_9_46_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.59.1758"},{"key":"e_1_2_9_47_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.77.3865"},{"key":"e_1_2_9_48_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.78.1396"},{"key":"e_1_2_9_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scriptamat.2015.07.021"},{"key":"e_1_2_9_50_1","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.73.515"},{"key":"e_1_2_9_51_1","doi-asserted-by":"publisher","DOI":"10.21105\/joss.01361"},{"key":"e_1_2_9_52_1","doi-asserted-by":"publisher","DOI":"10.1039\/C4EE03389B"},{"key":"e_1_2_9_53_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.97.170201"},{"key":"e_1_2_9_54_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.4812323"},{"key":"e_1_2_9_55_1","first-page":"2579","volume":"9","author":"van der Maaten L.","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_2_9_56_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.inorgchem.1c03879"}],"container-title":["Advanced Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/advanced.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/aisy.202300141","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/advanced.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/aisy.202300141","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T22:41:19Z","timestamp":1759876879000},"score":1,"resource":{"primary":{"URL":"https:\/\/advanced.onlinelibrary.wiley.com\/doi\/10.1002\/aisy.202300141"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,10]]},"references-count":55,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["10.1002\/aisy.202300141"],"URL":"https:\/\/doi.org\/10.1002\/aisy.202300141","archive":["Portico"],"relation":{},"ISSN":["2640-4567","2640-4567"],"issn-type":[{"value":"2640-4567","type":"print"},{"value":"2640-4567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,10]]},"assertion":[{"value":"2023-03-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-10-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"2300141"}}