The paper is a perspective that summarizes MOF design principles for atmospheric water harvesting and discusses the potential of AI to accelerate discovery of improved sorbents.
Predicting Scale-Up of Metal-Organic Framework Syntheses with Large Language Models
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abstract
Scalable synthesis remains the gate between MOF discovery and industrial deployment, as scale-up know-how is fragmented across disparate reports. We introduce ESU-MOF, a literature-mined dataset and a positive-unlabeled learning strategy that fine-tunes large language models to predict scalability potential with 91.4% accuracy, enabling rapid data-driven triage for industrial MOF discovery.
fields
cond-mat.mtrl-sci 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Sustainable Metal-Organic Framework Water Harvesters in the Artificial Intelligence Era
The paper is a perspective that summarizes MOF design principles for atmospheric water harvesting and discusses the potential of AI to accelerate discovery of improved sorbents.