Application of machine-learning-based global optimization: potential-dependent co-electrosorbed structure and activity on the Pd(110) surface

Abstract

Electrodes can adsorb different reaction intermediates under electrochemical conditions, which in turn significantly affect their electrochemical performance. This complex phenomenon attracts continuous interest in both science and industry for understanding the co-electrosorbed structure and activity under electrochemical conditions. Here, we report the first theoretical attempt by combining the machine-learning-based global optimization (SSW-NN method) and modified Poisson–Boltzmann continuum solvation (CM-MPB) based on first-principles calculations to elucidate the potential-dependent co-electrosorbed species on the Pd(110) surface. We reveal the potential-dependence adsorption/absorption hydrogen phases, the phase transition of α-H_ri/Pd to β-H_ri/Pd, and the co-electrosorbed H_ri-NH_y surface structures. In particular, we found that H_ri-NH₂ and H_ri-NH₃ are favorable intermediates for the N₂ reduction reaction, and the subsurface H is the key species responsible for NH₂ hydrogenation on the Pd(110) electrode.