Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics

Amorphous
silicon (a-Si) is a widely studied noncrystalline
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 using a machine-learning-based interatomic
potential. Our best a-Si network is obtained by simulated
cooling from the melt at a rate of 1011 K/s (that is, on
the 10 ns time scale), contains less than 2% defects, and agrees with
experiments regarding excess energies, diffraction data, and 29Si NMR chemical shifts. We show that this level of quality
is impossible to achieve with faster quench simulations. We then generate
a 4096-atom system that 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 potentials for elucidating
structures and properties of technologically important amorphous materials.