Machine learning-assisted design of transition metal-doped 2D WSn₂N₄ electrocatalysts for enhanced hydrogen evolution reaction
Guang Wang, Yi Wang, Yi Wang, Yingchao Wang, Yingchao Wang, Teng‐Teng Chen, Lei Li, Zhengli Zhang, Zhao Ding, Xiang Guo, Zijiang Luo, Xuefei Liu
Abstract
Hydrogen energy is characterized as environmentally friendly and resourceful. The hydrogen evolution reaction (HER) is a crucial process for hydrogen production and is essential for achieving a transition to sustainable and clean energy. In this work, we conducted a systematic investigation into the hydrogen catalytic activity of two-dimensional WSn 2 N 4 materials. The transition metal atoms (Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn) were substituted at the N and Sn sites in WSn 2 N 4 , respectively. The catalyst's performance was evaluated to verify its catalytic activity for the HER. The results indicate that the Gibbs free energy changes (ΔG H∗ ) of Ti@W–WSn 2 N 4 , V@W–WSn 2 N 4 , Fe@W–WSn 2 N 4 , Co@W–WSn 2 N 4 , and Ni@W–WSn 2 N 4 are close to zero. Among the structures examined, Ti@W–WSn 2 N 4 exhibited the lowest Gibbs free energy change (ΔG H∗ = −0.01 eV), indicating a high degree of catalytic activity. Through machine learning analysis, key features affecting catalytic activity could be directly identified, and a framework for rapid screening was established. This study lays a solid foundation for the design and development of potential HER catalysts in the future.