REANN 2.0: An efficient package of neural network potentials for multi-element systems

Recursively embedded atom neural network (REANN) is a general-purpose atomistic machine learning software package for representing potential energy and other physical properties. The original REANN 1.0 architecture is a physically inspired invariant message passing neural network, which was designed for systems with a limited number of elements. It is efficient but hardly transferable to more complex multielement systems. In this work, we release REANN 2.0 aimed at multi-element systems and universal potentials, which integrates element embedding and equivariant representation. Compared to the first version, REANN 2.0 demonstrates enhanced element transferability and higher accuracy across various periodic systems with higher efficiency. Built upon this framework, a pre-trained REANN-MPtrj model without fine-tuning accurately predicts the lithium-ion diffusion dynamics in a benchmark solid-state electrolyte Li3YCl6. We hope this open-source software package will facilitate the development of computationally efficient universal potentials in the future.