DFT and Machine Learning Insights into Ru(II) Complex-Catalyzed Transfer Hydrogenation

Gui-Xiang Zhou; Cheng Hou

doi:10.1039/d6qo00178e

January 1, 2026Organic Chemistry Frontiers

DFT and Machine Learning Insights into Ru(II) Complex-Catalyzed Transfer Hydrogenation

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Key Points

This research aims to investigate the process and efficiency of Ru(II) complex-catalyzed transfer hydrogenation.
Utilized density functional theory (DFT) for computational analysis
Applied machine learning techniques to identify patterns in catalysis
Conducted experiments focusing on the reduction of carbonyl compounds
Demonstrated effective reduction of various carbonyl compounds
Identified optimal conditions for transfer hydrogenation
Revealed insights into catalyst behavior using machine learning analysis

Abstract

Transfer hydrogenation (TH), as a mild reduction strategy that avoids the use of high-pressure molecular hydrogen, has attracted significant interest for the selective reduction of carbonyl compounds. In this study,...

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Cite This Study

Zhou et al. (2026) studied this question.

synapsesocial.com/papers/69c8c25dde0f0f753b39cb43 https://doi.org/10.1039/d6qo00178e

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DFT and Machine Learning Insights into Ru(II) Complex-Catalyzed Transfer Hydrogenation

Key Points

Abstract

Discussion

Cite This Study

DFT and Machine Learning Insights into Ru(II) Complex-Catalyzed Transfer Hydrogenation

Key Points

Abstract

Discussion

Cite This Study

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