Unfortunately, such atomic-scale investigations are severely challenged by growth at liquid catalysts. In situ measurements not only have to deal with the reactive gas-phase conditions but also with the elevated temperatures, e.g., 1400 K in the case of Cu . On the modeling and simulation side, predictive-quality first-principles calculations are the major contemporary workhorse for atomic-scale mechanistic studies at solid metal catalysts. Yet, they impose excessive computational costs for liquid metal surfaces. Capturing the increased dynamics and reduced symmetries of a liquid metal surface requires sampling over length and time scales far beyond the often statically performed calculations at rigid active site motifs in solid catalyst studies. Furthermore, already the latter calculations are at present only practically feasible within semi-local DFT. To reach a high predictive quality on the basis of the involved approximate DFT functionals, much knowledge has been gathered about their applicability to different classes of reactions at solid catalysts. However, how much of this knowledge can be transferred to the liquid state is presently unclear. In this situation, not even the choice of the functional on which one would base first-principles simulations of growth at a liquid catalyst could be well motivated.