Quantum Dynamics of Water from Møller-Plesset Perturbation Theory via a Neural Network Potential

Abstract

We report the static and dynamical properties of liquid water at the level of second-order Møller-Plesset perturbation theory (MP2) with classical and quantum nuclear dynamics using a neural network potential. We examined the temperature-dependent radial distribution functions, diffusion, and vibrational dynamics. MP2 theory predicts over-structured liquid water as well as a lower diffusion coefficient at ambient conditions compared to experiments, which may be attributed to the incomplete basis set. A better agreement with experimental structural properties and the diffusion constant are observed at an elevated temperature of 340 K from our simulations. Although the high-level electronic structure calculations are expensive, training a neural network potential requires only a few thousand frames. This approach shows great potential, requiring modest human effort, and is straightforwardly extensible to other simple liquids.