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Review Article | Open Access

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

Yue Liua,d, Zhengwei Yanga, Zhenyao Yua, Zitu Liua, Dahui Liua, Hailong Linb, Mingqing Lib, Shuchang Maa, Maxim Avdeeve,f, Siqi Shib,c,( )
School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
State Key Laboratory of Advanced Special Steel, School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, China
Materials Genome Institute, Shanghai University, Shanghai, 200444, China
Shanghai Engineering Research Center of Intelligent Computing System, Shanghai, 200444, China
Australian Nuclear Science and Technology Organisation, Sydney, 2232, Australia
School of Chemistry, The University of Sydney, Sydney, 2006, Australia

Peer review under responsibility of The Chinese Ceramic Society.

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Graphical Abstract

Abstract

Generative 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.

Keywords

Artificial intelligenceDeep learningMachine learningGenerative artificial intelligenceMaterials scienceNovel materials discovery

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Journal of Materiomics
Volume 9 Issue 4,
July 2023
Pages 798-816
DOI: 10.1016/j.jmat.2023.05.001
Cite this article:
Liu Y, Yang Z, Yu Z, et al. Generative artificial intelligence and its applications in materials science: Current situation and future perspectives. Journal of Materiomics, 2023, 9(4): 798-816. https://doi.org/10.1016/j.jmat.2023.05.001

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Received: 02 May 2023
Revised: 10 May 2023
Accepted: 12 May 2023
Published: 25 May 2023
© 2023 The Authors.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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