Sustainable Metal-Organic Framework Water Harvesters in the Artificial Intelligence Era
Abstract
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.
Versions
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BibTeX
@misc{coyle2026sustainable,
title = {{Sustainable Metal-Organic Framework Water Harvesters in the Artificial Intelligence Era}},
author = {Reid A. Coyle and Shyam Chand Pal and Peter Walther and Saeun Park and Bin Feng and Zhiling Zheng},
howpublished = {arXiv},
year = {2026},
month = jun,
doi = {10.48550/arxiv.2605.29179},
eprint = {2605.29179},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.29179},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Sustainable Metal-Organic Framework Water Harvesters in the Artificial Intelligence Era
AU - Reid A. Coyle
AU - Shyam Chand Pal
AU - Peter Walther
AU - Saeun Park
AU - Bin Feng
AU - Zhiling Zheng
PY - 2026
DA - 2026/06/15
PB - arXiv
DO - 10.48550/arxiv.2605.29179
UR - https://arxiv.org/abs/2605.29179
AB - 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.
ER -