Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation

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, labeling, and training. Application to the sample systems of Al, Mg and Al-Mg alloys demonstrates that DP-GEN can generate uniformly accurate PES models with a minimum number of labeled data.