Active learning of uniformly accurate interatomic 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, 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.

• UD
  U.S. Department of Energy
• NN
  National Natural Science Foundation of China

  Awards: 11871110, U1430237, U1501501, 11501039, 91530322, 91130005
• OO
  Office of Naval Research

  Award: N00014-13-1-0338