Deep Potential: A General Representation of a Many-Body Potential Energy Surface (Communications in Computational Physics, 2018)
We present a simple, yet general, deep neural network representation of thepotential energy surface for atomic and molecular systems. It is "first-principle" based,in the sense that no ad hoc approximations or empirical fitting functions are required.When tested on a wide variety of examples, it reproduces the original model withinchemical accuracy. This brings us one step closer to carrying out molecular simulationswith quantum mechanics accuracy at empirical potential computational cost.