Sustainable Metal-Organic Framework Water Harvesters in the Artificial Intelligence Era
For researchers in MOF-based water harvesting, this provides a forward-looking overview of AI integration, but the contribution is incremental as it synthesizes existing knowledge without new experimental or computational findings.
This Perspective reviews MOF design principles for atmospheric water harvesting and discusses how AI, LLMs, and data mining can accelerate discovery of high-performance sorbents. No concrete results are presented.
Metal-organic frameworks (MOFs) are excellent 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 further 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 cooperative adsorption, operational relative humidity (RH), uptake capacity, hysteresis, and scalability. We highlight recent design advancements such as multivariate strategies and long-arm linker extension, and examine how these principles tune pore capacity and hydrophilicity, while preserving stability and crystallinity. Furthermore, we discuss how AI, large language models (LLMs), and data mining can accelerate the discovery process through predictive synthesis, inverse design, and elucidating synthesis-structure-property relationships for the next generation of MOF water harvesters.