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Metal surfaces have long been known to reconstruct, significantly influencingtheir structural and catalytic properties. Many key mechanistic aspects ofthese subtle transformations remain poorly understood due to limitations ofprevious simulation approaches. Using active learning of Bayesianmachine-learned force fields trained from ab initio calculations, we enablelarge-scale molecular dynamics simulations to describe the thermodynamics andtime evolution of the low-index mesoscopic surface reconstructions of Au (e.g.,the Au(111)-`Herringbone,' Au(110)-(1$\times$2)-`Missing-Row,' andAu(100)-`Quasi-Hexagonal' reconstructions). This capability yields directatomistic understanding of the dynamic emergence of these surface states fromtheir initial facets, providing previously inaccessible information such asnucleation kinetics and a complete mechanistic interpretation of reconstructionunder the effects of strain and local deviations from the originalstoichiometry. We successfully reproduce previous experimental observations ofreconstructions on pristine surfaces and provide quantitative predictions ofthe emergence of spinodal decomposition and localized reconstruction inresponse to strain at non-ideal stoichiometries. A unified mechanisticexplanation is presented of the kinetic and thermodynamic factors drivingsurface reconstruction. Furthermore, we study surface reconstructions on Aunanoparticles, where characteristic (111) and (100) reconstructionsspontaneously appear on a variety of high-symmetry particle morphologies.

Stability, mechanisms and kinetics of emergence of Au surface reconstructions using Bayesian force fields