Application of machine-learning-based global optimization: potential-dependent co-electrosorbed structure and...

Area of Science:

• Surface science and electrochemistry
• Computational materials science
• Catalysis

Background:

• Electrode performance is significantly influenced by adsorbed reaction intermediates.
• Understanding co-electrosorbed structures and activities is vital for both scientific and industrial applications.
• The Pd(110) surface is a key catalyst in various electrochemical processes.

Purpose of the Study:

• To theoretically elucidate potential-dependent co-electrosorbed species on the Pd(110) surface.
• To investigate the adsorption and absorption of hydrogen phases.
• To identify favorable intermediates for the nitrogen (N2) reduction reaction.

Main Methods:

• Combining machine-learning-based global optimization (SSW-NN method).
• Utilizing modified Poisson-Boltzmann continuum solvation (CM-MPB).
• Employing first-principles calculations.

Main Results:

• Revealed potential-dependent adsorption/absorption hydrogen phases on Pd(110).
• Identified the phase transition from α-H/Pd to β-H/Pd.
• Discovered co-electrosorbed H-NH2 and H-NH3 surface structures as favorable intermediates for N2 reduction.
• Found subsurface hydrogen to be key for NH2 hydrogenation.

Conclusions:

• The study provides a theoretical framework for understanding complex electrochemical adsorption phenomena.
• Identified specific hydrogen-ammonia intermediates crucial for nitrogen reduction reactions.
• Subsurface hydrogen plays a critical role in the hydrogenation of intermediates on Pd(110).