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 bring...
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-02-01
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| Series: | APL Materials |
| Online Access: | http://dx.doi.org/10.1063/5.0253847 |
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| _version_ | 1849707329094680576 |
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| author | Mengfan Wu Shiyi Zhang Jie Ren |
| author_facet | Mengfan Wu Shiyi Zhang Jie Ren |
| author_sort | Mengfan Wu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-993bbdcdd2d746098d792f611f48592c |
| institution | DOAJ |
| issn | 2166-532X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | APL Materials |
| spelling | doaj-art-993bbdcdd2d746098d792f611f48592c2025-08-20T03:15:57ZengAIP Publishing LLCAPL Materials2166-532X2025-02-01132020601020601-2110.1063/5.0253847AI-empowered digital design of zeolites: Progress, challenges, and perspectivesMengfan Wu0Shiyi Zhang1Jie Ren2Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 200092, ChinaSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaShanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 200092, ChinaThe 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.http://dx.doi.org/10.1063/5.0253847 |
| spellingShingle | Mengfan Wu Shiyi Zhang Jie Ren AI-empowered digital design of zeolites: Progress, challenges, and perspectives APL Materials |
| title | AI-empowered digital design of zeolites: Progress, challenges, and perspectives |
| title_full | AI-empowered digital design of zeolites: Progress, challenges, and perspectives |
| title_fullStr | AI-empowered digital design of zeolites: Progress, challenges, and perspectives |
| title_full_unstemmed | AI-empowered digital design of zeolites: Progress, challenges, and perspectives |
| title_short | AI-empowered digital design of zeolites: Progress, challenges, and perspectives |
| title_sort | ai empowered digital design of zeolites progress challenges and perspectives |
| url | http://dx.doi.org/10.1063/5.0253847 |
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