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

A variety of complexation, reconstruction, and sulfide formation processes can occur at step edges on the {111} surfaces of coinage metals (M) in the presence of adsorbed S under ultra-high vacuum conditions. Given the cooperative many-atom nature of these reaction processes, Molecular Dynamics (MD) simulation of the associated dynamics is instructive. However, only quite restricted Density Functional Theory (DFT)-level ab initio MD is viable. Thus, for M = Ag and Cu, we instead utilize the DeePMD framework to develop machine-learning derived potentials, retaining near-DFT accuracy for the M-S systems, which should have broad applicability. These potentials are validated by comparison with DFT predictions for various key quantities related to the energetics of S on M(111) surfaces. The potentials are then utilized to perform extensive MD simulations elucidating the above diverse restructuring and reaction processes at step edges. Key observations from MD simulations include the formation of small metal-sulfur complexes, especially MS2; development of a local reconstruction at A-steps featuring an S-decorated {100} motif; and 3D sulfide formation. Additional analysis yields further information on the kinetics for metal-sulfur complex formation, where these complexes can strongly enhance surface mass transport, and on the propensity for sulfide formation.

Automated summary

Complexation, reconstruction, and sulfide formation processes occur at step edges on the {111} surfaces of coinage metals in the presence of adsorbed S. Molecular Dynamics simulation and machine learning derived potentials are used to study and validate these processes. Key observations include the formation of metal-sulfur complexes, local reconstruction at step edges, and 3D sulfide formation.