  Machine learning methods have nowadays become easy-to-use tools for
constructing high-dimensional interatomic potentials with ab initio accuracy.
Although machine learned interatomic potentials are generally orders of
magnitude faster than first-principles calculations, they remain much slower
than classical force fields, at the price of using more complex structural
descriptors. To bridge this efficiency gap, we propose an embedded atom neural
network approach with simple piecewise switching function based descriptors,
resulting in a favorable linear scaling with the number of neighbor atoms.
Numerical examples validate that this piecewise machine learning model can be
over an order of magnitude faster than various popular machine learned
potentials with comparable accuracy for both metallic and covalent materials,
approaching the speed of the fastest embedded atom method (i.e. several
{\mu}s/atom per CPU core). The extreme efficiency of this approach promises its
potential in first-principles atomistic simulations of very large systems
and/or in long timescale.
