It is worth noting that there are certain similarities between semi-local and hybrid functionals regarding the choice of the damping term. Specically, the use of Becke-Johnson (BJ) damping tends to increase the density of ice compared to water. While this adversely affects the performance of semi-local functionals, it has a somewhat positive eect on hybrid functionals, restoring the density maximum. But now the density dierence between ice and water is again too small, so the agreement with the experiment is still far from good. Empirically mixing the machine-learning potentials for revPBE0-D3 with zero damping and revPBE0-D3 with Becke-Johnson damping yields the most favorable outcome (revPBE0-D3/mix). To do so, the energies, forces, and stress tensors in the two datasets are mixed using a weighting factor 1/2. This is equivalent to recalculating the entire database with a damping function between the two choices. This approach demonstrates excellent agreement in predicting the density dierence between ice and water and results in a well-dened density maximum, albeit at temperatures that are somewhat lower than experimentally observed, and with the maximum in density occurring below the melting point. We estimate that a similar mixture for PBE0-D3 would also improve upon the standard dampings yielding a 1.5% too large density and an isobar maximum around 315 K.