  A general method to obtain a representation of the structural landscape of
nanoparticles in terms of a limited number of variables is proposed. The method
is applied to a large dataset of parallel tempering molecular dynamics
simulations of gold clusters of 90 and 147 atoms, silver clusters of 147 atoms,
and copper clusters of 147 atoms, covering a plethora of structures and
temperatures. The method leverages convolutional neural networks to learn the
radial distribution functions of the nanoclusters and to distill a
low-dimensional chart of the structural landscape. This strategy is found to
give rise to a physically meaningful and differentiable mapping of the atom
positions to a low-dimensional manifold, in which the main structural motifs
are clearly discriminated and meaningfully ordered. Furthermore, unsupervised
clustering on the low-dimensional data proved effective at further splitting
the motifs into structural subfamilies characterized by very fine and
physically relevant differences, such as the presence of specific punctual or
planar defects or of atoms with particular coordination features. Owing to
these peculiarities, the chart also enabled tracking of the complex structural
evolution in a reactive trajectory. In addition to visualization and analysis
of complex structural landscapes, the presented approach offers a general,
low-dimensional set of differentiable variables which has the potential to be
used for exploration and enhanced sampling purposes.
