Application of machine-learning-based global optimization: potential-dependent co-electrosorbed structure and activity on the Pd(110) surface. Issue 31 (27th July 2022)

• Record Type: Journal Article
• Authors: Wang, Li-Yuan; Fang, Ya-Hui
• Is Part Of: Physical chemistry chemical physics. Volume 24:Issue 31(2022)
• Page Start: 18523
• Page End: 18528
• Publication Date: 2022-07-27
• Subjects: Chemistry, Physical and theoretical -- Periodicals; 541.3
• DOI: 10.1039/d2cp01610a
• Languages: English
• ISSNs: 1463-9076

Abstract

Abstract : Electrodes can adsorb different reaction intermediates under electrochemical conditions, which in turn significantly affect their electrochemical performance. 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 r i /Pd to β-H r i /Pd, and the co-electrosorbed H r i -NH y surface structures. In particular, we found that H r i -NH2 and H r i -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.