  We propose a simple, but efficient and accurate machine learning (ML) model
for developing high-dimensional potential energy surface. This so-called
embedded atom neural network (EANN) approach is inspired by the well-known
empirical embedded atom method (EAM) model used in condensed phase. It simply
replaces the scalar embedded atom density in EAM with a Gaussian-type orbital
based density vector, and represents the complex relationship between the
embedded density vector and atomic energy by neural networks. We demonstrate
that the EANN approach is equally accurate as several established ML models in
representing both big molecular and extended periodic systems, yet with much
fewer parameters and configurations. It is highly efficient as it implicitly
contains the three-body information without an explicit sum of the conventional
costly angular descriptors. With high accuracy and efficiency, EANN potentials
can vastly accelerate molecular dynamics and spectroscopic simulations in
complex systems at ab initio level.
