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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
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…
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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.
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Submitted 15 June, 2026; v1 submitted 27 May, 2026;
originally announced May 2026.
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Predicting Scale-Up of Metal-Organic Framework Syntheses with Large Language Models
Authors:
Peter Walther,
Hongrui Sheng,
Xinxin Liu,
Bin Feng,
Reid Coyle,
Xinhua Yan,
Kyle Smith,
Harrison Kayal,
Shyam Chand Pal,
Zhiling Zheng
Abstract:
Scalable synthesis remains the gate between MOF discovery and industrial deployment, as scale-up know-how is fragmented across disparate reports. We introduce ScaleMOF, a literature-mined dataset and a positive-unlabeled learning strategy that fine-tunes large language models. Achieving 93.5% accuracy, this proof-of-concept serves as a literature-grounded ranking tool prioritizing plausible scale-…
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Scalable synthesis remains the gate between MOF discovery and industrial deployment, as scale-up know-how is fragmented across disparate reports. We introduce ScaleMOF, a literature-mined dataset and a positive-unlabeled learning strategy that fine-tunes large language models. Achieving 93.5% accuracy, this proof-of-concept serves as a literature-grounded ranking tool prioritizing plausible scale-up candidates.
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Submitted 8 July, 2026; v1 submitted 21 April, 2026;
originally announced April 2026.
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The Rise of Generative AI for Metal-Organic Framework Design and Synthesis
Authors:
Chenru Duan,
Aditya Nandy,
Shyam Chand Pal,
Xin Yang,
Wenhao Gao,
Yuanqi Du,
Hendrik Kraß,
Yeonghun Kang,
Varinia Bernales,
Zuyang Ye,
Tristan Pyle,
Ray Yang,
Zeqi Gu,
Philippe Schwaller,
Shengqian Ma,
Shijing Sun,
Alán Aspuru-Guzik,
Seyed Mohamad Moosavi,
Robert Wexler,
Zhiling Zheng
Abstract:
Advances in generative artificial intelligence are transforming how metal-organic frameworks (MOFs) are designed and discovered. This Perspective introduces the shift from laborious enumeration of MOF candidates to generative approaches that can autonomously propose and synthesize in the laboratory new porous reticular structures on demand. We outline the progress of employing deep learning models…
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Advances in generative artificial intelligence are transforming how metal-organic frameworks (MOFs) are designed and discovered. This Perspective introduces the shift from laborious enumeration of MOF candidates to generative approaches that can autonomously propose and synthesize in the laboratory new porous reticular structures on demand. We outline the progress of employing deep learning models, such as variational autoencoders, diffusion models, and large language model-based agents, that are fueled by the growing amount of available data from the MOF community and suggest novel crystalline materials designs. These generative tools can be combined with high-throughput computational screening and even automated experiments to form accelerated, closed-loop discovery pipelines. The result is a new paradigm for reticular chemistry in which AI algorithms more efficiently direct the search for high-performance MOF materials for clean air and energy applications. Finally, we highlight remaining challenges such as synthetic feasibility, dataset diversity, and the need for further integration of domain knowledge.
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Submitted 15 August, 2025;
originally announced August 2025.