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.
| Original language | English |
|---|---|
| Pages (from-to) | 213-226 |
| Number of pages | 14 |
| Journal | Chinese Journal of Catalysis |
| Volume | 80 |
| DOIs | |
| State | Published - Jan 2026 |
| Externally published | Yes |
Keywords
- Hydrogen evolution reaction
- Interpretable machine learning
- Single-atom catalyst
- Spin density
- Symmetry breaking
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