The quality assertion notably extends to the systematic error due to the employed approximate DFT functional and dispersion correction. The possibility to train ML-potentials with DFT data computed at different levels of theory and then efficiently compute liquid state properties allows to analyze the respective suitability in an analog way as traditionally done through static direct DFT calculations in solid-state catalysis. For Gr on liquid Cu, such analysis showed a strong correlation to the performance of short-range functionals and their combination with various dispersion corrections known from Gr on solid Cu. If these correlations prevail for other systems, future DFT-based studies of synthesis of 2D materials can rely their choice of functional either on descriptors which are computationally much easier to obtain on solid surfaces, or the existing community knowledge built up over decades of work on solid-state catalysts.