  In recent years, promising deep learning based interatomic potential energy
surface (PES) models have been proposed that can potentially allow us to
perform molecular dynamics simulations for large scale systems with quantum
accuracy. However, making these models truly reliable and practically useful is
still a very non-trivial task. A key component in this task is the generation
of datasets used in model training. In this paper, we introduce the Deep
Potential GENerator (DP-GEN), an open-source software platform that implements
the recently proposed "on-the-fly" learning procedure [Phys. Rev. Materials 3,
023804] and is capable of generating uniformly accurate deep learning based PES
models in a way that minimizes human intervention and the computational cost
for data generation and model training. DP-GEN automatically and iteratively
performs three steps: exploration, labeling, and training. It supports various
popular packages for these three steps: LAMMPS for exploration, Quantum
Espresso, VASP, CP2K, etc. for labeling, and DeePMD-kit for training. It also
allows automatic job submission and result collection on different types of
machines, such as high performance clusters and cloud machines, and is adaptive
to different job management tools, including Slurm, PBS, and LSF. As a concrete
example, we illustrate the details of the process for generating a
general-purpose PES model for Cu using DP-GEN.
