Generative artificial intelligence and its applications in materials science: current situation and future perspectives

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dc.contributor.authorLiu, Yen_AU
dc.contributor.authorYang, ZWen_AU
dc.contributor.authorYu, ZYen_AU
dc.contributor.authorLiu, Zen_AU
dc.contributor.authorLiu, Den_AU
dc.contributor.authorLin, Hen_AU
dc.contributor.authorLi, MQen_AU
dc.contributor.authorMa, Sen_AU
dc.contributor.authorAvdeev, Men_AU
dc.contributor.authorShi, SQen_AU
dc.date.accessioned2024-08-22T02:11:13Zen_AU
dc.date.available2024-08-22T02:11:13Zen_AU
dc.date.issued2023-07en_AU
dc.date.statistics2024-07-18en_AU
dc.description.abstractGenerative Artificial Intelligence (GAI) is attracting the increasing attention of materials community for its excellent capability of generating required contents. With the introduction of Prompt paradigm and reinforcement learning from human feedback (RLHF), GAI shifts from the task-specific to general pattern gradually, enabling to tackle multiple complicated tasks involved in resolving the structure-activity relationships. Here, we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of methodology. The applications of task-specific generative models involving materials inverse design and data augmentation are also dissected. Taking ChatGPT as an example, we explore the potential applications of general GAI in generating multiple materials content, solving differential equation as well as querying materials FAQs. Furthermore, we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding solutions. This work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development. © 2023 The Authors. Published by Elsevier B.V. on behalf of The Chinese Ceramic Society. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_AU
dc.description.sponsorshipThis work was supported in part by National Natural Science Foundation of China [grant number 92270124, 52073169] and National Key Research and Development Program of China [grant number 2021YFB3802101] the Key Research Project of Zhejiang Laboratory [grant number 2021PE0AC02]. We appreciated the High-Performance Computing Center of Shanghai University and Shanghai Engineering Research Center of Intelligent Computing System for providing the computing resources and technical support.en_AU
dc.identifier.citationLiu, Y., Yang, Z., Yu, Z., Liu, Z., Liu, D., Lin, H., Li, M., Ma, S., Avdeev, M., & Shi, S. (2023). Generative artificial intelligence and its applications in materials science: current situation and future perspectives. Journal of Materiomics, 9(4), 798-816. doi:10.1016/j.jmat.2023.05.001en_AU
dc.identifier.issn2352-8478en_AU
dc.identifier.issn2352-8486en_AU
dc.identifier.issue4en_AU
dc.identifier.journaltitleJournal of Materiomicsen_AU
dc.identifier.pagination798-816en_AU
dc.identifier.urihttps://doi.org/10.1016/j.jmat.2023.05.001en_AU
dc.identifier.urihttps://apo.ansto.gov.au/handle/10238/15658en_AU
dc.identifier.volume9en_AU
dc.languageEnglishen_AU
dc.language.isoenen_AU
dc.publisherElsevieren_AU
dc.subjectArtificial intelligenceen_AU
dc.subjectStructure-activity relationshipsen_AU
dc.subjectDataen_AU
dc.subjectData analysisen_AU
dc.subjectAugmentationen_AU
dc.subjectMachine Learningen_AU
dc.subjectLearningen_AU
dc.subjectMaterialsen_AU
dc.titleGenerative artificial intelligence and its applications in materials science: current situation and future perspectivesen_AU
dc.typeJournal Articleen_AU
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