  Few questions in condensed matter science have proven as difficult to unravel
as the interplay between structure and dynamics in supercooled liquids and
glasses. The conundrum: close to the glass transition, the dynamics slow down
dramatically and become heterogeneous while the structure appears largely
unperturbed. Largely unperturbed, however, is not the same as unperturbed, and
many studies have attempted to identify "slow" local structures by exploiting
dynamical information. Nonetheless, the question remains open: is the key to
the slow dynamics imprinted in purely structural information? And if so, is
there a way to determine the relevant structures without any dynamical
information? Here, we use a newly developed unsupervised machine learning (UML)
algorithm to identify structural heterogeneities in three archetypical glass
formers. In each system, the UML approach autonomously designs an order
parameter based purely on structural variation within a single snapshot.
Impressively, this order parameter strongly correlates with the dynamical
heterogeneity. Moreover, the structural characteristics linked to slow
particles disappear as we move away from the glass transition. Our results
demonstrate the power of machine learning techniques to detect structural
patterns even in disordered systems, and provide a new way forward for
unraveling the structural origins of the slow dynamics of glassy materials.
