Accelerating Discovery of Water Stable Metal-Organic Frameworks by Machine Learning

ZM Zhang and FS Pan and SA Mohamed and CX Ji and K Zhang and JW Jiang and ZY Jiang, SMALL, 20 (2024).

DOI: 10.1002/smll.202405087

Metal-organic frameworks (MOFs) provide an extensive design landscape for nanoporous materials that drive innovation across energy and environmental fields. However, their practical applications are often hindered by water stability challenges. In this study, a machine learning (ML) approach is proposed to accelerate the discovery of water stable MOFs and validated through experimental test. First, the largest database currently available that contains water stability information of 1133 synthesized MOFs is constructed and categorized according to experimental stability. Then, structural and chemical descriptors are applied at various fragmental levels to develop ML classifiers for predicting the water stability of MOFs. The ML classifiers achieve high prediction accuracy and excellent transferability on out-of-sample validation. Next, two MOFs are experimentally synthesized with their water stability tested to validate ML predictions. Finally, the ML classifiers are applied to discover water stable MOFs in the ab initio REPEAT charge MOF (ARC-MOF) database. Among approximate to 280 000 candidates, approximate to 130 000 (47%) MOFs are predicted to be water stable; furthermore, through multi-stability analysis, 461 (0.16%) MOFs are identified as not only water stable but also thermal and activation stable. The ML approach is anticipated to serve as a prerequisite filtering tool to streamline the exploration of water stable MOFs for important practical applications. This study integrates machine learning with experimental validation, aiming to accelerate the discovery of water stable metal-organic frameworks. The developed machine learning approach demonstrates high accuracy, streamlined scalability, and robust prediction capability toward important water related applications. image

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