Interpretation of surface diffusion data with Langevin simulations - a quantitative assessment

TL;DR

This study evaluates the accuracy of 2D Langevin simulations for surface diffusion by comparing them with molecular dynamics, finding Langevin methods sufficiently accurate and highlighting the importance of temperature-dependent friction in data interpretation.

Contribution

It provides a quantitative assessment of Langevin simulations' validity in surface diffusion studies, comparing them with molecular dynamics to confirm their reliability.

Findings

Langevin simulations closely match molecular dynamics results within explored parameters.

Correlations between substrate and adsorbate have minimal impact on observables.

Temperature-dependent friction significantly influences diffusion observable calculations.

Abstract

Diffusion studies of adsorbates moving on a surface are often analyzed using 2D Langevin simulations. These simulations are computationally cheap and offer valuable insight into the dynamics, however, they simplify the complex interactions between the substrate and adsorbate atoms, neglecting correlations in the motion of the two species. The effect of this simplification on the accuracy of observables extracted using Langevin simulations was previously unquantified. Here we report a numerical study aimed at assessing the validity of this approach. We compared experimentally accessible observables which were calculated using a Langevin simulation with those obtained from explicit molecular dynamics simulations. Our results show that within the range of parameters we explored Langevin simulations provide a good alternative for calculating the diffusion procress, i.e. the effect of correlations is too small to be observed within the numerical accuracy of this study and most likely would not have a significant effect on the interpretation of experimental data. Our comparison of the two numerical approaches also demonstrates the effect temperature dependent friction has on the calculated observables, illustrating the importance of accounting for such a temperature dependence when interpreting experimental data.