With large enough training sets, the GemNet potentials used herein do display impressive performance, however. This is demonstrated by applications in hightemperature MD simulations of systems that are significantly larger than the training molecules. In this extrapolative regime, errors are mostly systematic and explainable and no instabilities were observed. Interestingly, no significant improvements in terms of accuracy or robustness were observed when training on 3.2Mio instead of 320k samples, indicating that all relevant information about the underlying potential energy surface can be learned from less than 10% of the dataset. This is significant because robust ML potentials are often associated with iterative training procedures. Due to their size and complexity (the models used herein fit 2.2 million parameters), GNN models are a priori not ideal for such settings. Indeed, training times of several GPU weeks are not unusual, which is clearly impractical in an iterative workflow. Well curated databases like QM7-x and powerful model architectures like GemNet circumvent this issue.