AI-driven advances in metal–organic frameworks: from data to design and applications by Yuhang Song & Jiakai Li & Dongzhi Chi & Zhengtao Xu & Jie Liu & Mingxi Chen & Ziyu Wang instant download

AI-driven advances in metal–organic frameworks: from data to design and applications by Yuhang Song & Jiakai Li & Dongzhi Chi & Zhengtao Xu & Jie Liu & Mingxi Chen & Ziyu Wang instant download

Metal–organic frameworks (MOFs) are a versatile class of porous materials with unprecedented structuraltunability, surface area, and application potential in areas such as gas storage, carbon capture, and biomedicine. However, their immense chemical design space poses significant challenges for conventional discovery and optimization methods. Recent advances in artificial intelligence (AI) and machine learning (ML)have introduced transformative capabilities to this field, enabling accurate property prediction, automatedstructure generation, and synthesis planning at scale. This review provides a comprehensive overview ofAI-driven strategies for accelerating MOF research. It discusses key databases, deep learning architectures,generative models, and hybrid AI-simulation frameworks that have reshaped the design and screening ofhigh-performance MOFs. Techniques such as graph neural networks and AL have enabled breakthroughsReceived 25th July 2025,in structure–property prediction, while integration with robotics is advancing autonomous laboratories.Accepted 17th September 2025Despite these advancements, challenges remain in data quality, model interpretability, and experimentalDOI: 10.1039/d5cc04220hvalidation. Future directions include physics-informed ML models, standardized data protocols, and deeperintegration of AI with chemical robotics. By highlighting both opportunities and current limitations, thisrsc.li/chemcommreview aims to provide a roadmap for the next generation of AI-accelerated MOF innovation.