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

被引:0
|
作者
Wu, Mengfan [1 ,2 ]
Zhang, Shiyi [3 ]
Ren, Jie [1 ,2 ]
机构
[1] Tongji Univ, Shanghai Res Inst Intelligent Autonomous Syst, Shanghai 200092, Peoples R China
[2] Tongji Univ, Ctr Phonon & Thermal Energy Sci, Sch Phys Sci & Engn, Shanghai Key Lab Special Artificial Microstruct Ma, Shanghai 200092, Peoples R China
[3] Shanghai Jiao Tong Univ, Sch Biomed Engn, Shanghai 200240, Peoples R China
来源
APL MATERIALS | 2025年 / 13卷 / 02期
基金
上海市自然科学基金; 中国国家自然科学基金;
关键词
HIGH-THROUGHPUT SYNTHESIS; MACHINE LEARNING APPROACH; PURE-SILICA ZEOLITES; MECHANICAL-PROPERTIES; GAS-STORAGE; HETEROGENEOUS CATALYSIS; SYNTHESIS DESCRIPTORS; ION-EXCHANGE; DISCOVERY; PREDICTION;
D O I
10.1063/5.0253847
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
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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收藏
页数:21
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