  The structure of amorphous silicon is widely thought of as a
fourfold-connected random network, and yet it is defective atoms, with fewer or
more than four bonds, that make it particularly interesting. Despite many
attempts to explain such "dangling-bond" and "floating-bond" defects,
respectively, a unified understanding is still missing. Here, we show that
atomistic machine-learning methods can reveal the complex structural and
energetic landscape of defects in amorphous silicon. We study an
ultra-large-scale, quantum-accurate structural model containing a million
atoms, and more than ten thousand defects, allowing reliable defect-related
statistics to be obtained. We combine structural descriptors and
machine-learned local atomic energies to develop a universal classification of
the different types of defects in amorphous silicon. The results suggest a
revision of the established floating-bond model by showing that
fivefold-coordinated atoms in amorphous silicon exhibit a wide range of local
environments, and it is shown that fivefold (but not threefold) coordination
defects tend to cluster together. Our study provides new insights into one of
the most widely studied amorphous solids, and has general implications for
modelling and understanding defects in disordered materials beyond silicon
alone.
