Sustainable Metal–Organic Framework Water Harvesters in the Artificial Intelligence Era

Abstract

Metal–organic frameworks (MOFs) are promising candidates for water harvesting due to their tunable pore environments, which can be precisely engineered to capture and release water in arid conditions. Integrating artificial intelligence (AI) into MOF discovery can accelerate the design of high-performance sorbents by identifying structural features that enhance atmospheric water harvesting (AWH), stability, and cycling efficiency. In this Perspective, we examine key MOF design principles, including the adsorption site, operational relative humidity (RH), uptake capacity, hysteresis, and scalability. We highlight recent design advancements such as multivariate strategies and long-arm linker extension and illustrate how these principles tune pore capacity and hydrophilicity while preserving stability and crystallinity. Furthermore, we discuss how machine learning (ML) and large language models (LLMs) can accelerate the discovery of next-generation MOF water harvesters through predictive synthesis, inverse design, and elucidating synthesis–structure–property relationships for water-harvesting MOFs.