Reconstruction of low-index Au surfaces using large-scale machine learning molecular dynamicswith many-body Bayesian force fields

Gold surfaces have a long history of microscopic studies due to their propensity to reconstruct in complex patterns depending on the terminating surface facet and the environment (temperatures and atmosphere). However, the details of restructuring processes and metastable intermediate structures, which could be critical for catalytic reactions, remain unknown due to the limits of microscopy time resolution and accuracy of classical force field simulations. 

This work demonstrates a robust many-body Bayesian potential trained with an on-the-fly active learning framework implemented using Gaussian process regression in the FLARE code [1]. The active learning module uses molecular dynamics (MD) simulations of low-index surfaces to sample important configurations and runs density functional theory (DFT) calculations when the prediction Bayesian uncertainty exceeds a threshold. The trained Bayesian force field is then used to perform a large-scale MD to study surface diffusion and reconstructions of various surface facets, e.g., Herringbone reconstruction on (111) and missing-row reconstruction on (110).  

  

 [1] J. Vandermause et al,  NPJ Computational Materials 6 (2020).