Machine learning guided discovery of water stable metal–organic frameworks for photocatalytic hydrogen production

Abstract

With remarkably tunable porosity and modular chemistry, metal–organic frameworks (MOFs) present a versatile platform for photocatalytic hydrogen (H₂) production. However, identifying high-performing and water stable MOFs from the vast design space is challenging. In this study, we develop a hierarchical screening strategy to accelerate the discovery of photocatalytically active MOFs with robust water stability. First, machine learning (ML) classifiers are trained on experimental H₂ production data to predict photocatalytic performance, achieving high accuracy and excellent transferability. Then, starting from 11660 structures in the CoRE-MOF database, 1731 are shortlisted to be photocatalytically active. Detailed structure–performance analyses reveal that linker flexibility and aliphatic character positively correlate with H₂ evolution activity, while excessive aromaticity and rigidity are detrimental. Finally, a water stability classifier is applied to further identify 419 MOFs to be simultaneously photocatalytically promising and water stable. The ML-guided strategy provides a quantitative and interpretable path toward the discovery of new MOFs as photocatalysts, and it would facilitate future experimental exploration for efficient photocatalytic H₂ production.