Synthesis of 2D materials at liquid metal catalysts is a promising route to the large-scale production of these unique materials with very high quality. In particular, atomic-scale insight into why the growth at liquid surfaces excels the growth at solid surfaces could be critical for the further optimization of the process. To this end, we have established first-principles trained ML-potentials as a powerful approach that reliably transfers the predictive quality to the extensive sampling required to capture the liquid catalyst state in molecular simulations. Providing a firm experimental reference through the latest advanced in situ XRR measurements, we could show that such MLpotential-based simulations can provide the height of graphene above a catalytic liquid Cu surface with sub-Angstrom precision. The ability to predict quantitatively such a core quantity for 2D materials growth is a prerequisite to future detailed mechanistic investigations.