Chinese Journal of Catalysis ›› 2026, Vol. 80: 213-226.DOI: 10.1016/S1872-2067(25)64837-6

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Spin density symmetry breaking-mediated hydrogen evolution in single-atom catalysts

Xin Songa, Zhonghua Lia,*(), Li Shenga,*(), Yang Liub,*()   

  1. aSchool of Chemistry and Chemical Engineering, Key Laboratory of Microsystems and Microstructures Manufacturing, Ministry of Education, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China
    bSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China
  • Received:2025-06-20 Accepted:2025-08-08 Online:2026-01-18 Published:2026-01-05
  • Contact: Zhonghua Li, Li Sheng, Yang Liu
  • Supported by:
    National Natural Science Foundation of China(51272052);National Natural Science Foundation of China(50902040);National Natural Science Foundation of China(62071154);Natural Science Foundation of Heilongjiang Province of China(LH2020B011);Natural Science Foundation of Heilongjiang Province of China(LH2019B006);Scientific Research Projects of Basic Scientific Research Operational Expenses of Heilongjiang Provincial Colleges and Universities(2021-KYYWF-0171)

Abstract:

Symmetry-broken single-atom catalysts (SACs) are pivotal due to their asymmetric electronic environments, which enhance the activity of the hydrogen evolution reaction (HER). This study investigated how symmetry breaking in SACs affects HER performance using density functional theory (DFT) and variable selection machine learning (ML). The study revealed a nearly volcano-shaped correlation between the degree of spin density symmetry breaking (Dasym) and HER activity, with catalysts at the base of the volcano showing enhanced HER activity. Spin density symmetry breaking facilitates the enrichment of unpaired electrons on the active sites and reduces HER energy barriers, resulting in up to a 40-fold enhancement in HER performance of symmetry-broken SACs compared to symmetric SACs. The ML model accurately identified key descriptors, such as symmetry breaking and electronic transfer effects, allowing spin density symmetry breaking on M-N3C-SWCNTs to be further condensed into an effect term with a structure-property relationship. A weaker symmetry breaking effect and a stronger electron transfer enhance HER performance. ML-guided analysis highlighted a spin selection-related Volmer-Heyrovsky pathway with a dual activation mechanism involving surface atom displacement and para-activation. These findings offer critical insights into the design of advanced HER catalysts by elucidating the interplay between symmetry-breaking properties and catalytic behavior.

Key words: Symmetry breaking, Spin density, Hydrogen evolution reaction, Interpretable machine learning, Single-atom catalyst