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

[2605.29179] Sustainable Metal-Organic Framework Water Harvesters in the Artificial Intelligence Era Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Condensed Matter > Materials Science arXiv:2605.29179 (cond-mat) [Submitted on 27 May 2026 ( v1 ), last revised 15 Jun 2026 (this version, v2)] Title: Sustainable Metal-Organic Framework Water Harvesters in the Artificial Intelligence Era Authors: Reid A. Coyle , Shyam Chand Pal , Peter Walther , Saeun Park , Bin Feng , Zhiling Zheng View a PDF of the paper titled Sustainable Metal-Organic Framework Water Harvesters in the Artificial Intelligence Era, by Reid A. Coyle and 5 other authors View PDF 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. Comments: 10 pages of main text, 26 total pages. 3 Figures and 1 Table of Content Graphic Subjects: Materials Science (cond-mat.mtrl-sci) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2605.29179 [cond-mat.mtrl-sci] (or arXiv:2605.29179v2 [cond-mat.mtrl-sci] for this version) https://doi.org/10.48550/arXiv.2605.29179 Focus to learn more arXiv-issued DOI via DataCite Journal reference: ACS Materials Lett. 2026, 8, 9, 2391-2400 Related DOI : https://doi.org/10.1021/acsmaterialslett.6c00580 Focus to learn more DOI(s) linking to related resources Submission history From: Reid Coyle [ view email ] [v1] Wed, 27 May 2026 23:30:45 UTC (709 KB) [v2] Mon, 15 Jun 2026 23:39:02 UTC (709 KB) Full-text links: Access Paper: View a PDF of the paper titled Sustainable Metal-Organic Framework Water Harvesters in the Artificial Intelligence Era, by Reid A. 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