Active Learning of Uniformly Accurate Deep Potential Models for Multicomponent Systems

We propose an active learning procedure called Deep Potential Generator (DP-GEN) for the construction of accurate and transferable potential energy surface (PES) models. This procedure has three major components: exploration, labeling, and training. As an important application, we use DP-GEN to generate an ab-initio trained reactive force field for water that describes both the molecular and the dissociated water phases.