{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T12:38:57Z","timestamp":1778589537456,"version":"3.51.4"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"30","license":[{"start":{"date-parts":[[2025,9,13]],"date-time":"2025-09-13T00:00:00Z","timestamp":1757721600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,13]],"date-time":"2025-09-13T00:00:00Z","timestamp":1757721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Science and Technology projects of Yunnan Precious Metals Laboratory","award":["YPML-2023050209"],"award-info":[{"award-number":["YPML-2023050209"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s00521-025-11532-8","type":"journal-article","created":{"date-parts":[[2025,9,13]],"date-time":"2025-09-13T16:47:30Z","timestamp":1757782050000},"page":"25061-25076","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Graph isomorphism attention network combined with pre-trained language models: a novel approach for crystal material property prediction"],"prefix":"10.1007","volume":"37","author":[{"given":"Jiahao","family":"Kang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingjie","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongfei","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junpeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,13]]},"reference":[{"key":"11532_CR1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.120.145301","volume":"120","author":"T Xie","year":"2018","unstructured":"Xie T, Grossman JC (2018) Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett 120:145301. https:\/\/doi.org\/10.1103\/PhysRevLett.120.145301","journal-title":"Phys Rev Lett"},{"issue":"9","key":"11532_CR2","doi-asserted-by":"publisher","first-page":"3564","DOI":"10.1021\/acs.chemmater.9b01294","volume":"31","author":"C Chen","year":"2019","unstructured":"Chen C, Ye W, Zuo Y, Zheng C, Ong SP (2019) Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater 31(9):3564\u20133572. https:\/\/doi.org\/10.1021\/acs.chemmater.9b01294","journal-title":"Chem Mater"},{"key":"11532_CR3","doi-asserted-by":"publisher","first-page":"18141","DOI":"10.1039\/D0CP01474E","volume":"22","author":"S-Y Louis","year":"2020","unstructured":"Louis S-Y, Zhao Y, Nasiri A, Wang X, Song Y, Liu F, Hu J (2020) Graph convolutional neural networks with global attention for improved materials property prediction. Phys Chem Chem Phys 22:18141\u201318148. https:\/\/doi.org\/10.1039\/D0CP01474E","journal-title":"Phys Chem Chem Phys"},{"key":"11532_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.commatsci.2023.112619","volume":"233","author":"J Xiao","year":"2024","unstructured":"Xiao J, Yang L, Wang S (2024) Graph isomorphism network for materials property prediction along with explainability analysis. Comput Mater Sci 233:112619. https:\/\/doi.org\/10.1016\/j.commatsci.2023.112619","journal-title":"Comput Mater Sci"},{"issue":"1","key":"11532_CR5","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1038\/s41524-021-00650-1","volume":"7","author":"K Choudhary","year":"2021","unstructured":"Choudhary K, DeCost B (2021) Atomistic line graph neural network for improved materials property predictions. NPJ Comput Mater 7(1):185. https:\/\/doi.org\/10.1038\/s41524-021-00650-1","journal-title":"NPJ Comput Mater"},{"issue":"1","key":"11532_CR6","doi-asserted-by":"publisher","DOI":"10.1063\/1.4812323","volume":"1","author":"A Jain","year":"2013","unstructured":"Jain A, Ong SP, Hautier G, Chen W, Richards WD, Dacek S, Cholia S, Gunter D, Skinner D, Ceder G, Persson KA (2013) Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater 1(1):011002. https:\/\/doi.org\/10.1063\/1.4812323 (https:\/\/pubs.aip.org\/aip\/apm\/article-pdf\/doi\/10.1063\/1.4812323\/13163869\/011002_1_online.pdf)","journal-title":"APL Mater"},{"issue":"1","key":"11532_CR7","doi-asserted-by":"publisher","first-page":"5179","DOI":"10.1038\/s41598-017-05402-0","volume":"7","author":"K Choudhary","year":"2017","unstructured":"Choudhary K, Kalish I, Beams R, Tavazza F (2017) High-throughput identification and characterization of two-dimensional materials using density functional theory. Sci Rep 7(1):5179","journal-title":"Sci Rep"},{"key":"11532_CR8","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations. https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"11532_CR9","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2018) Graph attention networks. In: International conference on learning representations. https:\/\/openreview.net\/forum?id=rJXMpikCZ"},{"issue":"5","key":"11532_CR10","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1002\/wcms.1125","volume":"3","author":"J Neugebauer","year":"2013","unstructured":"Neugebauer J, Hickel T (2013) Density functional theory in materials science. WIREs Comput Mol Sci 3(5):438\u2013448. https:\/\/doi.org\/10.1002\/wcms.1125 (https:\/\/wires.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/wcms.1125)","journal-title":"WIREs Comput Mol Sci"},{"issue":"1","key":"11532_CR11","doi-asserted-by":"publisher","first-page":"34256","DOI":"10.1038\/srep34256","volume":"6","author":"M Jong","year":"2016","unstructured":"Jong M, Chen W, Notestine R, Persson K, Ceder G, Jain A, Asta M, Gamst A (2016) A statistical learning framework for materials science: application to elastic moduli of k-nary inorganic polycrystalline compounds. Sci Rep 6(1):34256. https:\/\/doi.org\/10.1038\/srep34256","journal-title":"Sci Rep"},{"key":"11532_CR12","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd international conference on international conference on machine learning. ICML\u201915, , vol 37, pp 448\u2013456"},{"key":"11532_CR13","first-page":"7464","volume":"34","author":"D Cai","year":"2020","unstructured":"Cai D, Lam W (2020) Graph transformer for graph-to-sequence learning. Proc AAAI Conf Artif Intell 34:7464\u20137471","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"11532_CR14","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3950755","author":"N Walker","year":"2021","unstructured":"Walker N, Trewartha A, Huo H, Lee S, Cruse K, Dagdelen J, Dunn A, Persson K, Ceder G, Jain A (2021) The impact of domain-specific pre-training on named entity recognition tasks in materials science. SSRN Electron J. https:\/\/doi.org\/10.2139\/ssrn.3950755","journal-title":"SSRN Electron J"},{"issue":"1","key":"11532_CR15","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1038\/s43246-022-00315-6","volume":"3","author":"P Reiser","year":"2022","unstructured":"Reiser P, Neubert M, Eberhard A, Torresi L, Zhou C, Shao C, Metni H, Hoesel C, Schopmans H, Sommer T, Friederich P (2022) Graph neural networks for materials science and chemistry. Commun Mater 3(1):93. https:\/\/doi.org\/10.1038\/s43246-022-00315-6","journal-title":"Commun Mater"},{"key":"11532_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ddtec.2020.11.009","volume":"37","author":"O Wieder","year":"2020","unstructured":"Wieder O, Kohlbacher S, Kuenemann M, Garon A, Ducrot P, Seidel T, Langer T (2020) A compact review of molecular property prediction with graph neural networks. Drug Discov Today Technol 37:1\u201312. https:\/\/doi.org\/10.1016\/j.ddtec.2020.11.009","journal-title":"Drug Discov Today Technol"},{"issue":"5","key":"11532_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2022.100491","volume":"3","author":"SS Omee","year":"2022","unstructured":"Omee SS, Louis S-Y, Fu N, Wei L, Dey S, Dong R, Li Q, Hu J (2022) Scalable deeper graph neural networks for high-performance materials property prediction. Patterns 3(5):100491. https:\/\/doi.org\/10.1016\/j.patter.2022.100491","journal-title":"Patterns"},{"key":"11532_CR18","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein J, Doran C, Solorio T (eds) Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, vol 1 (long and short papers). Association for Computational Linguistics, Minneapolis, pp 4171\u20134186. https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"issue":"8","key":"11532_CR19","doi-asserted-by":"publisher","first-page":"5789","DOI":"10.1007\/s10462-021-09958-2","volume":"54","author":"FA Acheampong","year":"2021","unstructured":"Acheampong FA, Nunoo-Mensah H, Chen W (2021) Transformer models for text-based emotion detection: a review of BERT-based approaches. Artif Intell Rev 54(8):5789\u20135829. https:\/\/doi.org\/10.1007\/s10462-021-09958-2","journal-title":"Artif Intell Rev"},{"key":"11532_CR20","doi-asserted-by":"publisher","unstructured":"Qin X, Wu Z, Zhang T, Li Y, Luan J, Wang B, Wang L, Cui J (2023) Bert-erc: fine-tuning bert is enough for emotion recognition in conversation. In: Proceedings of the thirty-seventh AAAI conference on artificial intelligence and thirty-fifth conference on innovative applications of artificial intelligence and thirteenth symposium on educational advances in artificial intelligence. AAAI\u201923\/IAAI\u201923\/EAAI\u201923. AAAI Press. https:\/\/doi.org\/10.1609\/aaai.v37i11.26582","DOI":"10.1609\/aaai.v37i11.26582"},{"key":"11532_CR21","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1016\/j.neunet.2022.03.017","volume":"150","author":"P Kumar","year":"2022","unstructured":"Kumar P, Raman B (2022) A bert based dual-channel explainable text emotion recognition system. Neural Netw 150:392\u2013407. https:\/\/doi.org\/10.1016\/j.neunet.2022.03.017","journal-title":"Neural Netw"},{"issue":"15","key":"11532_CR22","doi-asserted-by":"publisher","DOI":"10.1063\/5.0142150","volume":"122","author":"Z Wang","year":"2023","unstructured":"Wang Z, Ma J, Hu R, Luo X (2023) Predicting lattice thermal conductivity of semiconductors from atomic-information-enhanced cgcnn combined with transfer learning. Appl Phys Lett 122(15):152106. https:\/\/doi.org\/10.1063\/5.0142150 (https:\/\/pubs.aip.org\/aip\/apl\/article-pdf\/doi\/10.1063\/5.0142150\/16821009\/152106_1_5.0142150.pdf)","journal-title":"Appl Phys Lett"},{"key":"11532_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.ces.2022.117813","volume":"259","author":"X Lu","year":"2022","unstructured":"Lu X, Xie Z, Wu X, Li M, Cai W (2022) Hydrogen storage metal-organic framework classification models based on crystal graph convolutional neural networks. Chem Eng Sci 259:117813. https:\/\/doi.org\/10.1016\/j.ces.2022.117813","journal-title":"Chem Eng Sci"},{"key":"11532_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.commatsci.2021.110314","volume":"190","author":"J Lee","year":"2021","unstructured":"Lee J, Asahi R (2021) Transfer learning for materials informatics using crystal graph convolutional neural network. Comput Mater Sci 190:110314. https:\/\/doi.org\/10.1016\/j.commatsci.2021.110314","journal-title":"Comput Mater Sci"},{"key":"11532_CR25","unstructured":"Sch\u00fctt KT, Kindermans P-J, Sauceda HE, Chmiela S, Tkatchenko A, M\u00fcller K-R (2017) Schnet: a continuous-filter convolutional neural network for modeling quantum interactions. In: Proceedings of the 31st international conference on neural information processing systems. NIPS\u201917. Curran Associates Inc., Red Hook, pp 992\u20131002"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11532-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11532-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11532-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T12:38:11Z","timestamp":1760013491000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11532-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,13]]},"references-count":25,"journal-issue":{"issue":"30","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["11532"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11532-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,13]]},"assertion":[{"value":"17 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}