  Gold nanoclusters have been the focus of numerous computational studies but
an atomistic understanding of their structural and dynamical properties at
finite temperature is far from satisfactory. To address this deficiency, we
investigate gold nanoclusters via ab initio molecular dynamics, in a range of
sizes where a core-shell morphology is observed. We analyze their structure and
dynamics using of state-of-the-art techniques, including unsupervised
machine-learning nonlinear dimensionality reduction (sketch-map) for describing
the similarities and differences among the range of sampled configurations. We
find that, whereas the gold nanoclusters exhibit continuous structural
rearrangement, they clearly show persistent motifs: a cationic core of one to
five atoms is loosely bound to a shell which typically displays a substructure
resulting from the competition between locally spherical vs planar fragments.
Besides illuminating the properties of core-shell gold nanoclusters, the
present study proposes a set of useful tools for understanding their nature in
operando.
