Research reviews AI's impact on function optimization and design of covalent organic frameworks, suggesting significant advancements in applications across multiple fields.
In recent years, the popularity of artificial intelligence (AI) has been rising, and its application fields have been expanding. In the field of covalent organic frameworks (COFs), AI can efficiently and quickly assist the screening, prediction, design, and optimization of COFs, thus expanding the application field of COFs. This paper systematically reviews the research progress in the application of AI in COFs in recent 5 years (2021–2025), and focuses on how machine learning (ML) and other algorithms can empower the structural design, performance prediction, and function optimization of COFs, so as to efficiently prepare target materials. And focus on its cross‐disciplinary applications in life and health, food, environment, energy and so on. In the end, the research trend of AI technology in COFs is prospected, and the core challenges faced by this cross‐field research (such as the shortage of interdisciplinary talents) are analyzed, so as to provide some reference for related research.
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Zhao et al. (2025) studied this question.
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