On the evaluation of hydrogen evolution reaction performance of metal-nitrogen-doped carbon electrocatalysts using machine learning technique

Single-atom catalysts (SACs) introduce as a promising category of electrocatalysts, especially in the water-splitting process. Recent studies have exhibited that nitrogen-doped carbon-based SACs can act as a great HER electrocatalyst. In this regard, Adaptive Neuro-Fuzzy Inference optimized by Gray Wolf Optimization (GWO) method was used to predict hydrogen adsorption energy (ΔG) obtained from density functional theory (DFT) for single transition-metal atoms including Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Zr, Nb, Mo, Tc, Ru, Rh, Pd, Ag, Cd, Hf, Ta, W, Re, Os, Ir, Pt, and Au embedded in N-doped carbon of different sizes. Various descriptors such as the covalent radius, Zunger radius of the atomic d-orbital, the formation energy of the single-atom site, ionization energy, electronegativity, the d-band center from − 6 to 6 eV, number of valence electrons, Bader charge, number of occupied d states from 0 to − 2 eV, and number of unoccupied d states from 0 to 2 eV were chosen as input parameters based on sensitivity analysis. The R-squared and MSE of the developed model were 0.967 and 0.029, respectively, confirming its great accuracy in determining hydrogen adsorption energy of metal/NC electrocatalysts.