  In this work, we present a general purpose deep neural network package for
representing energies, forces, dipole moments, and polarizabilities of
atomistic systems. This so-called recursively embedded atom neural network
model takes both advantages of the physically inspired atomic descriptor based
neural networks and the message-passing based neural networks. Implemented in
the PyTorch framework, the training process is parallelized on both CPU and GPU
with high efficiency and low memory, in which all hyperparameters can be
optimized automatically. We demonstrate the state-of-the-art accuracy, high
efficiency, scalability, and universality of this package by learning not only
energies (with or without forces), but also dipole moment vectors and
polarizability tensors, in various molecular, reactive, and periodic systems.
An interface between a trained model and LAMMPs is provided for large scale
molecular dynamics simulations. We hope that this open-source toolbox will
allow future method development and applications of machine learned potential
energy surfaces and quantum-chemical properties of molecules, reactions, and
materials.
