Metal–Organic Frameworks for Water Harvesting: Machine Learning-Based Prediction and Rapid Screening

Abstract

Atmospheric water harvesting based on metal–organic frameworks (MOFs) is an emerging technology to potentially mitigate water scarcity. Because of the tremendously large number of existing MOFs, it is challenging to find suitable candidates. In this context, a data-driven approach to identify top-performing MOFs represents an important direction. Herein, we develop a machine learning (ML) method to predict water adsorption in MOFs and screen out top-performing MOFs for water harvesting. First, experimental water adsorption isotherms in MOFs are collected and water adsorption properties are extracted. Quantitative structure–property relationships are analyzed in terms of pore structure and framework chemistry, providing task-specific design principles. Then, ML models are trained and interpreted to predict water adsorption properties by using structural and chemical features, as well as operating conditions as descriptors. The transferability of the ML models is validated by out-of-sample predictions in seven newly reported MOFs. Finally, the ML models are applied to screen ∼8000 “Computation-Ready, Experimental” (CoRE) MOFs. Top-performing candidates are identified including 149 MOFs with the maximum adsorption capacity ≥35 mmol/g, 39 MOFs with working capacity ≥10 mmol/g in a relative pressure window 0.1–0.3, and 139 MOFs with working capacity ≥8.7 mmol/g in a relative pressure window 0.6–0.9. The developed ML-based method would advance task-oriented design and rapid discovery of reticular materials for energy and environmental applications.