Generative artificial intelligence and its applications in materials science: Current situation and future perspectives
Open accessAbstract
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.
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The energy required to run LLMs and associated web-serving infrastructure is a limitation.
“Adding to that, another limitation is the energy required to run LLMs and the associated web-serving infrastructure (Liu et al. 2023a ).”
From A review of LLMs and their applications in the architecture, engineering and construction industry (2025) · cites this paper as Liu et al. 2023a
Cites
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Generative AI for crystal structures: A review
De Breuck, P.-P., Wang, H.-C., Rignanese, G.-M., Botti, S., & Marques, M. A. L. (2025) · npj Computational Materials · doi:10.1038/s41524-025-01881-2 · Cited by 24 · see citing works
- Authors
- Liu, Y., Yang, Z., Yu, Z., Liu, Z., Liu, D., Lin, H., Li, M., Ma, S., Avdeev, M., & Shi, S.
- Affiliations
- Shanghai University (CN)
- The University of Sydney (AU)
- Corresponding author
- Shi, Siqi
- ORCID
- Venue
- Journal of Materiomics
- Publisher
- Elsevier BV
- Published
- 2023-07
- Volume
- 9
- Issue
- 4
- Pages
- 798-816
- ISSN
- 2352-8478 (ISSN-L)
- OpenAlex
- W4378188762
- Cited by
- 294
- References
- 8
- Citations by year
- 2026: 59 · 2025: 111 · 2024: 114 · 2023: 10
- FWCI
- 21.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Materiomics (publishedVersion, cc-by-nc-nd)
- The Australian Nuclear Science and Technology Organisation Institutional Repository (The Australian Nuclear Science and Technology Organisation) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (52073169, 92270124)
- National Key Research and Development Program of China (2021YFB3802101)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- generative AI, materials science, reinforcement learning from human feedback, data augmentation, ChatGPT, structure-activity relationship
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