Water is ubiquitous on our planet, and life on Earth is unthinkable without water. It is not surprising, therefore, that water has received a great deal of attention from the scientic community. It was also one of the rst liquids to be intensively studied by computer simulations . Today, carefully constructed force elds play a key role in the study of water . The best force elds are constructed to give an excellent description of all relevant properties of water (density, volume, isobars, phase transitions, etc.) while also giving outstanding agreement with high-level quantum chemistry calculations for small clusters. More recently, there has been a gradual shift toward supplementing or even substituting force elds with rst-principles electronic structure calculations. These calculations predominantly rely on density functional theory (DFT), with the initial studies emerging in the early 1990s . As computer power improved, the calculations moved towards relevant timescales and system sizes . The advent of machine learning potentials has led to a further leap in the number of properties that can be predicted and the timescales that can be simulated . Especially notable is the capability to simulate density isobars and melting temperatures within a matter of days or even hours, rendering the comparison of these properties with experimental data straightforward.