Active learning of uniformly accurate interatomic potentials for materials simulation

LF Zhang and DY Lin and H Wang and R Car and WN E, PHYSICAL REVIEW MATERIALS, 3, 023804 (2019).

DOI: 10.1103/PhysRevMaterials.3.023804

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.

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