  The concept of chemical bonding can ultimately be seen as a rationalization
of the recurring structural patterns observed in molecules and solids. Chemical
intuition is nothing but the ability to recognize and predict such patterns,
and how they transform into one another. Here we discuss how to use a computer
to identify atomic patterns automatically, so as to provide an algorithmic
definition of a bond based solely on structural information. We concentrate in
particular on hydrogen bonding -- a central concept to our understanding of the
physical chemistry of water, biological systems and many technologically
important materials. Since the hydrogen bond is a somewhat fuzzy entity that
covers a broad range of energies and distances, many different criteria have
been proposed and used over the years, based either on sophisticate electronic
structure calculations followed by an energy decomposition analysis, or on
somewhat arbitrary choices of a range of structural parameters that is deemed
to correspond to a hydrogen-bonded configuration. We introduce here a
definition that is univocal, unbiased, and adaptive, based on our
machine-learning analysis of an atomistic simulation. The strategy we propose
could be easily adapted to similar scenarios, where one has to recognize or
classify structural patterns in a material or chemical compound.
