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

Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm