  Automated analyses of the outcome of a simulation have been an important part
of atomistic modeling since the early days, addressing the need of linking the
behavior of individual atoms and the collective properties that are usually the
final quantity of interest. Methods such as clustering and dimensionality
reduction have been used to provide a simplified, coarse-grained representation
of the structure and dynamics of complex systems, from proteins to
nanoparticles. In recent years, the rise of machine learning has led to an even
more widespread use of these algorithms in atomistic modeling, and to consider
different classification and inference techniques as part of a coherent toolbox
of data-driven approaches.
  This perspective briefly reviews some of the unsupervised machine-learning
methods -- that are geared towards classification and coarse-graining of
molecular simulations -- seen in relation to the fundamental mathematical
concepts that underlie all machine-learning techniques. It discusses the
importance of using concise yet complete representations of atomic structures
as the starting point of the analyses, and highlights the risk of introducing
preconceived biases when using machine learning to rationalize and understand
structure-property relations. Supervised machine-learning techniques, that
explicitly attempt to predict the properties of a material given its structure,
are less susceptible to such biases. Current developments in the field suggest
that using these two classes of approaches side-by-side and in a fully
integrated mode, while keeping in mind the relations between the data analysis
framework and the fundamental physical principles, will be key for realizing
the full potential of machine learning to help understanding the behavior of
complex molecules and materials.
