Reaction processes at step edges on S-decorated Cu(111) and Ag(111) surfaces: MD analysis utilizing machine learning...

Area of Science:

• Surface Science
• Computational Materials Science
• Chemical Physics

Background:

• Step edges on coinage metal {111} surfaces exhibit complex reactions with adsorbed sulfur.
• Simulating these many-atom processes requires computationally intensive methods like ab initio Molecular Dynamics (MD).

Purpose of the Study:

• To develop accurate machine-learning potentials for simulating metal-sulfur dynamics on Ag and Cu {111} surfaces.
• To elucidate the complex restructuring and reaction pathways at metal surface step edges.

Main Methods:

• Developed machine-learning potentials using the DeePMD framework for Ag-S and Cu-S systems.
• Validated potentials against Density Functional Theory (DFT) calculations for energetics.
• Performed extensive MD simulations to study surface dynamics.

Main Results:

• Observed formation of metal-sulfur complexes, particularly MS₂.
• Identified local reconstruction at A-steps with an S-decorated {100} motif.
• Characterized the development of 3D sulfide structures and their kinetics.

Conclusions:

• Machine-learning potentials offer near-DFT accuracy for simulating complex metal-sulfur surface interactions.
• These simulations provide insights into surface mass transport enhancement and sulfide formation propensity.