It appears that even the tightest convergence thresholds used remain insufficient for model (I), as rather large changes are observed. The same behavior can be observed for systems (II) and (III), which all appear to converge slower than (IV) (cf. ESI † Fig. ). Thus we can conclude that deepening of the ML-tree structure by setting a threshold for the adjacency matrix may indeed improve convergence with respect to p NO , but at the time being it is not clear whether there is an optimal choice. Model (V) (having a comparatively flat tree) in Fig. (c) demonstrated by far the highest computational demand, combined with the slowest convergence behavior. In passing, we note that an ML-tree with 4 clusters at the All results are normalized to the model (IV) simulation for a threshold of p NO = 0.5%. top, followed by 3 in the second layer and 2 for all remaining ones was also tested. However, the convergence was too slow and the computational demand too high to obtain results in a time comparable to all other models.