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

X. Niu; Z. H. Zhang; Xiaoyu Wu; Yan Liu; Y Cui; Jianwen Jiang

doi:10.1039/d5sc08277c

January 1, 2026Chemical ScienceOpen Access

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

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Key Points

This research aims to explore machine learning techniques to identify water stable metal-organic frameworks (MOFs) that efficiently produce hydrogen through photocatalysis.
Developed a hierarchical machine learning strategy
Focused on the properties of metal-organic frameworks
Evaluated photocatalytic activity and water stability
Identified new MOFs with robust water stability
Demonstrated significant potential for hydrogen production
Accelerated the discovery process for photocatalytic materials

Implication

Machine learning discovers stable photocatalytic materials for hydrogen production, suggesting faster advancements in renewable energy.

Abstract

A hierarchical machine learning strategy is developed to accelerate the discovery of photocatalytically active MOFs with robust water stability for hydrogen production.

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Cite This Study

Niu et al. (2026) studied this question.

synapsesocial.com/papers/6997fa5aad1d9b11b34537bf https://doi.org/10.1039/d5sc08277c

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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