  Metal surfaces have long been known to reconstruct, significantly influencing
their structural and catalytic properties. Many key mechanistic aspects of
these subtle transformations remain poorly understood due to limitations of
previous simulation approaches. Using active learning of Bayesian
machine-learned force fields trained from ab initio calculations, we enable
large-scale molecular dynamics simulations to describe the thermodynamics and
time evolution of the low-index mesoscopic surface reconstructions of Au (e.g.,
the Au(111)-`Herringbone,' Au(110)-(1$\times$2)-`Missing-Row,' and
Au(100)-`Quasi-Hexagonal' reconstructions). This capability yields direct
atomistic understanding of the dynamic emergence of these surface states from
their initial facets, providing previously inaccessible information such as
nucleation kinetics and a complete mechanistic interpretation of reconstruction
under the effects of strain and local deviations from the original
stoichiometry. We successfully reproduce previous experimental observations of
reconstructions on pristine surfaces and provide quantitative predictions of
the emergence of spinodal decomposition and localized reconstruction in
response to strain at non-ideal stoichiometries. A unified mechanistic
explanation is presented of the kinetic and thermodynamic factors driving
surface reconstruction. Furthermore, we study surface reconstructions on Au
nanoparticles, where characteristic (111) and (100) reconstructions
spontaneously appear on a variety of high-symmetry particle morphologies.
