  Amorphous silicon (a-Si) is a widely studied non-crystalline material, and
yet the subtle details of its atomistic structure are still unclear. Here, we
show that accurate structural models of a-Si can be obtained by harnessing the
power of machine-learning algorithms to create interatomic potentials. Our best
a-Si network is obtained by cooling from the melt in molecular-dynamics
simulations, at a rate of 10$^{11}$ K/s (that is, on the 10 ns timescale). This
structure shows a defect concentration of below 2% and agrees with experiments
regarding excess energies, diffraction data, as well as $^{29}$Si solid-state
NMR chemical shifts. We show that this level of quality is impossible to
achieve with faster quench simulations. We then generate a 4,096-atom system
which correctly reproduces the magnitude of the first sharp diffraction peak
(FSDP) in the structure factor, achieving the closest agreement with
experiments to date. Our study demonstrates the broader impact of
machine-learning interatomic potentials for elucidating accurate structures and
properties of amorphous functional materials.
