Machine learning force fields (MLFF) are evolving rapidly and have been successfully employed over the past decade to perform molecular dynamics (MD) simulations with near-DFT accuracy over time-and length scales intractable for DFT based MD . The force fields can be trained on various data generated from, e.g., hand-crafted structures, molecular dynamics simulations, or randomly generated configurations, and often show remarkably good agreement with properties-including phonon dispersions-calculated from DFT . Recently, universal force fields have been developed and trained on data from the large database of geometry optimisations (energies, forces, stresses) found in the Materials Project . These have shown great promise for structural optimisation, lattice dynamics calculations and even MD simulations.