  An active learning procedure called Deep Potential Generator (DP-GEN) is
proposed for the construction of accurate and transferable machine
learning-based models of the potential energy surface (PES) for the molecular
modeling of materials. This procedure consists of three main components:
exploration, generation of accurate reference data, and training. Application
to the sample systems of Al, Mg and Al-Mg alloys demonstrates that DP-GEN can
produce uniformly accurate PES models with a minimal number of reference data.
