  We present a simple, yet general, end-to-end deep neural network
representation of the potential energy surface for atomic and molecular
systems. This methodology, which we call Deep Potential, is "first-principle"
based, in the sense that no ad hoc approximations or empirical fitting
functions are required. The neural network structure naturally respects the
underlying symmetries of the systems. When tested on a wide variety of
examples, Deep Potential is able to reproduce the original model, whether
empirical or quantum mechanics based, within chemical accuracy. The
computational cost of this new model is not substantially larger than that of
empirical force fields. In addition, the method has promising scalability
properties. This brings us one step closer to being able to carry out molecular
simulations with accuracy comparable to that of quantum mechanics models and
computational cost comparable to that of empirical potentials.
