Translating accurate electronic structure calculations into an accurate calculation of dynamical properties in liquid water via the neural network.

While first-principles molecular dynamics simulation has advanced greatly in the last few decades, accurate computation of the diffusion constant in liquid water remains highly challenging in practice. Underlying electronic structure calculation must be accurate, and the statistical sampling needs to be adequate enough at the same time. This is further complicated by the challenge of taking into account the nuclear quantum effect, which depends on both the underlying potential energy surface and the temperature. We will discuss how we are using the neural network potential for performing statistically-converged path integral molecular dynamics simulations in order to calculate self-diffusivity of liquid water for a wide range of temperatures. We will discuss successes and limitations of employing the neural network potential approach in this study.