We propose a scheme
for the automatic separation (i.e.,
clustering)
of data sets composed of several nanoparticle (NP) structures by means
of Machine Learning techniques. These data sets originate from atomistic
simulations, such as global optimizations searches and molecular dynamics
simulations, which can produce large outputs that are often difficult
to inspect by hand. By combining a description of NPs based on their
local atomic environment with unsupervised learning algorithms, such
as K-Means and Gaussian mixture model, we are able to distinguish
between different structural motifs (e.g., icosahedra, decahedra,
polyicosahedra, fcc fragments, twins, and so on). We show that this
method is able to improve over the results obtained previously thanks
to the successful implementation of a more detailed description of
NPs, especially for systems showing a large variety of structures,
including disordered ones.