AI-empowered digital design of zeolites: Progress, challenges, and perspectives
The rise of artificial intelligence (AI) as a powerful research tool in materials science has been extensively acknowledged. Particularly, exploring zeolites with target properties is of vital significance for industrial applications, integrating AI technologies into zeolite design undoubtedly brings immense promise for the advancements in this field. Here, we provide a comprehensive review in the AI-empowered digital design of zeolites. It showcases the state-of-the-art progress in predicting zeolite-related properties, employing machine learning potentials for zeolite simulations, using generative models for the inverse design, and aiding the experimental synthesis of zeolites. The challenges and perspectives are also discussed, emphasizing the new opportunities at the intersection of AI technologies and zeolites. This review is expected to offer crucial guidance for advancing innovations in materials science through AI in the future.
Authors
- Jie Ren (ORCID: https://orcid.org/0000-0003-2806-7226)
- Mengfan Wu (ORCID: https://orcid.org/0009-0005-8173-881X)
- Shiyi Zhang (ORCID: https://orcid.org/0000-0002-9375-5992)
Institutions
- Tongji University (CN)
- Shanghai Jiao Tong University (CN)
Publication Details
- Journal
- APL Materials
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1063/5.0253847
- Citations
- 8
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 4.14
Funders
- National Natural Science Foundation of China
- Program of Shanghai Academic Research Leader