材料科学
系统工程
纳米技术
工程物理
工程伦理学
工艺工程
工程类
作者
Mengfan Wu,Shiyi Zhang,Jie Ren
出处
期刊:APL Materials
[American Institute of Physics]
日期:2025-02-01
卷期号:13 (2)
被引量:8
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI