  The atomistic modeling of amorphous materials requires structure sizes and
sampling statistics that are challenging to achieve with first-principles
methods. Here, we propose a methodology to speed up the sampling of amorphous
and disordered materials using a combination of a genetic algorithm and a
specialized machine-learning potential based on artificial neural networks
(ANN). We show for the example of the amorphous LiSi alloy that around 1,000
first-principles calculations are sufficient for the ANN potential assisted
sampling of low-energy atomic configurations in the entire amorphous LixSi
phase space. The obtained phase diagram is validated by comparison with the
results from an extensive sampling of LixSi configurations using molecular
dynamics simulations and a general ANN potential trained to ~45,000
first-principles calculations. This demonstrates the utility of the approach
for the first-principles modeling of amorphous materials.
