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.

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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

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article

AI-empowered digital design of zeolites: Progress, challenges, and perspectives

Jie Ren, Mengfan Wu, Shiyi Zhang
8 citations
APL Materials
Machine Learning in Materials Science
4.14
article

AI-empowered digital design of zeolites: Progress, challenges, and perspectives

Jie Ren, Mengfan Wu, Shiyi Zhang
article en
8 citations

Abstract

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.

APL MaterialsVol. 13(2)
Tongji University (CN), Shanghai Jiao Tong University (CN)
National Natural Science Foundation of China, Program of Shanghai Academic Research Leader
Openalex Percentile: Top 6%
Machine Learning in Materials Science
4.14
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AI-empowered digital design of zeolites: Progress, challenges, and perspectives — Jie Ren, Mengfan Wu, et al. · APL Materials (2025) | TGRS Research Map | TGRS