Active spin state-derived descriptor for hydrogen evolution activity in 2D transition metal dichalcogenides
Spin states are pivotal in modulating the electrocatalytic activity of transition metal (TM)-based compounds, yet quantitatively evaluating the activity-spin state correlation remains a formidable challenge. Here, we propose an “activity index (η)” as a descriptor, to assess the activity of the spin states for the hydrogen evolution reaction (HER). η descriptor integrates three key electronic parameters: the proportion (P), broadening range (R), and center (εact) of the active spin state, which collectively account for the electronic structure modulation induced by both the intrinsic active site and its local coordination environment. Using 1T-phase ZrSe2-anchored TM atoms (TM = Sc to Ni) as prototypes, we reveal that the correlation between Gibbs free energy (ΔGH) and the η value follows a linear relation—ΔGH reduces as η decreases. Notably, ZrSe2-Mn exhibits the optimal η value (−0.56), corresponding to the best HER activity with a ΔGH of −0.04 eV—closer to the thermoneutral ideal value (0 eV) than even Pt (ΔGH = −0.09 eV). This relationship suggests that η is an effective descriptor of the active spin state for HER of TM-based catalysts. Our study establishes a quantitative correlation between HER activity and spin polarization, enabling performance prediction through spin-resolved physical descriptor.
Authors
- Qiaoling Wang (ORCID: https://orcid.org/0000-0002-7764-0685)
- Gui‐Fang Huang (ORCID: https://orcid.org/0000-0002-3450-0551)
- Tao Huang (ORCID: https://orcid.org/0000-0003-4153-3607)
- Yu Shyang Tan (ORCID: https://orcid.org/0000-0001-7282-8980)
- Wangyu Hu (ORCID: https://orcid.org/0000-0001-7416-3994)
- Wei‐Qing Huang (ORCID: https://orcid.org/0000-0002-9621-8332)
- Lei Li
- Junhang Luo (ORCID: https://orcid.org/0000-0002-5706-0990)
- Zixuan Yang
- Tao Zhang (ORCID: https://orcid.org/0000-0002-3151-1642)
Institutions
- Hunan University (CN)
- Division of Materials Science and Engineering (AU)
- Changsha Environmental Protection College (CN)
Publication Details
- Journal
- Applied Physics Letters
- Published
- 2025-10-20
- DOI
- https://doi.org/10.1063/5.0278112
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00