  Precise prediction of phase diagrams in molecular dynamics (MD) simulations
is challenging due to the simultaneous need for long time scales, large length
scales and accurate interatomic potentials. We show that thermodynamic
integration (TI) from low-cost force fields to neural network potentials (NNPs)
trained using density-functional theory (DFT) enables rapid first-principles
prediction of the solid-liquid phase boundary in the model salt NaCl. We use
this technique to compare the accuracy of several DFT exchange-correlation
functionals for predicting the NaCl phase boundary, and find that the inclusion
of dispersion interactions is critical to obtain good agreement with
experiment. Importantly, our approach introduces a method to predict
solid-liquid phase boundaries for any material at an ab-initio level of
accuracy, with the majority of the computational cost at the level of classical
potentials.
