  We use a structure analysis technique that quantifies cluster geometries
using machine learning to identify a bicapped square antiprism (BSAP) as the
nearest-neighbor cluster involved in the glass transition in Pd82Si18, a
prototype metal-metalloid metallic glass. BSAPs have slow dynamics and grow
significantly in concentration during cooling, like icosahedra in other
systems. These results are evidence that some preferred structures universally
contribute to the glass transition and show that analysis of interatomic
geometry can find important structures not revealed by topology.
