The effects of different exchange and correlation functionals on Neural Networks for water

Accurately obtaining the properties of bulk water, despite the apparent simplicity of the molecule, is still a challenge. Theoretically, the interplay of different types of interactions turn simulating macroscopic properties into a challenge. In a number of cases these properties require long timescales, and large simulation cells. In this work we obtain neural-network-trained force fields that are accurate at the level of Density Functional Theory (DFT). We compare different exchange and correlation potentials for properties of bulk water for properties that require large timescales. We show that the meta-GGA functional SCAN accurately predicts the properties of bulk water when care is taken in terms of system size, simulation time and including nuclear quantum effects.