Halide-induced Step Faceting and Dissolution Energetics from Atomistic Machine Learned Potentials on Cu(100).

ABSTRACT: Adsorbates impact the surface stability and reactivity of metallic electrodes, affecting the corrosion, dissolution, and deposition behavior. Here, we use density functional theory (DFT) and DFT-based Behler-Parrinello neural networks (BPNN) to investigate the geometries, surface formation energies, and atom removal energies of stepped and kinked surfaces vicinal to Cu(100) with a c(2×2) Cl adlayer. DFT calculations indicate that the stable structures for the adsorbate-free vicinal surfaces favor steps with <110> orientation, while the addition of the c(2×2) Cl adlayer leads to <100> step facets, in agreement with scanning tunneling microscopy (STM) observations. The BPNN calculations produce energies in good agreement with DFT results (root mean square error of 1.3 meV/atom for a randomly

SUBMITTER: Groenenboom MC 

PROVIDER: S-EPMC8240506 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature