The rational co-doping strategy of transition metal and non-metal atoms on g-CN for highly efficient hydrogen evolution by DFT and machine learning

As a clean energy source, hydrogen has attracted high interest in developing efficient hydrogen evolution reaction (HER) catalysts due to its sustainable and renewable characteristics. In this work, we have systematically investigated the HER activity of the g-CN two-dimensional materials. The catalytic activity in HER is enhanced by doping non-metallic atoms (C, N, B, Si) and transition metal atoms (Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn) in the vacancies of g-CN. Based on the first principle calculation, we screened 40 structures and found that the ΔGH* of Sc@C3–CN, V@C3–CN, Mn@C3–CN, Sc@N3–CN, and Ti@Si3–CN was close to zero. Among them, Ti@Si3–CN has the lowest Gibbs free energy change (−0.01 eV) and has excellent HER performance. In addition, we explored HER activity's origin by using machine learning (ML) algorithms. The results show that the atomic relative distance in the TM@X3-CN structure significantly affects the catalytic activity.