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

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, density functional theory (DFT) calculations combined with machine learning approaches were employed to systematically investigate the reaction mechanism of Ru(II)-complex-catalyzed base-free transfer hydrogenation under aerobic conditions. By comparing four plausible reaction pathways, the metal-ligand cooperative pathway involving the amide N-H site of the Ru(II) complex (Path D) was identified as the kinetically most favorable route, with the cooperative hydrogen transfer during the dehydrogenation of isopropanol being the rate-determining step. Distortion-interaction analysis, energy decomposition analysis (EDA), and Extended Transition State-Natural Orbitals for Chemical Valence (ETS-NOCV) results reveal that the participation of the auxiliary ligand N-H site in Path D effectively reduces the distortion energy of the transition state and enhances both orbital interactions and dispersion stabilization between the metal center and the substrate, thereby significantly stabilizing the key transition state. Machine learning analysis further indicates that the HOMO-LUMO gap and the metal atomic charge are the primary descriptors governing the reaction free energy barrier, while the reaction free energy of hydride intermediate formation (ΔG(Int2)) exhibits a strong linear correlation with the dehydrogenation barrier ΔG≠, serving as a quantitative predictor of catalytic activity and ligand effects. Overall, this study not only provides an electronic-structure-level understanding of the mechanistic origin and rate-controlling factors of metal-ligand cooperation(MLC) in transfer hydrogenation, but also establishes a transferable structure-reactivity relationship framework for the rational design of efficient transfer hydrogenation catalysts.

Supplementary files

Article information

Article type
Research Article
Submitted
10 Feb 2026
Accepted
26 Mar 2026
First published
27 Mar 2026

Org. Chem. Front., 2026, Accepted Manuscript

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

G. Zhou and C. Hou, Org. Chem. Front., 2026, Accepted Manuscript , DOI: 10.1039/D6QO00178E

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