  The structure of amorphous silicon (a-Si) has been studied for decades. The
two main theories are based on a continuous random network and on a
`paracrystalline' model, respectively -- the latter being defined as showing
localized structural order resembling the crystalline state whilst retaining an
overall amorphous network. However, the extent of this local order has been
unclear, and experimental data have led to conflicting interpretations. Here we
show that signatures of paracrystallinity in an otherwise disordered network
are indeed compatible with the existing body of experimental observations for
a-Si. We use quantum-mechanically accurate, machine-learning-driven simulations
to systematically sample the configurational space of quenched a-Si, thereby
allowing us to elucidate the boundary between amorphization and
crystallization. We analyze our dataset using structural and local-energy
descriptors to show that paracrystalline models are consistent with experiments
in both regards. Our work provides a unified explanation for seemingly
conflicting theories in one of the most widely studied amorphous networks.
