Consequently, this also limits the use of high levels of theory and restricts the achievable sampling. Previous works by our group and others 80,81 have demonstrated the efficiency and accuracy achievable using cluster data for training. When combined with active learning (AL), which iteratively builds the training set based on a preliminary version of the trained MLP, this approach yields accurate and data-efficient MLPs at a low computational cost. In this study, we expand this protocol to model metal complexes in solution, using clusters of solvated metal ions for training. Specifically, we apply Atomic Cluster Expansion (ACE) and its message-passing neural network-based variant (MACE) to two model systems, Mg 2+ complexes in aqueous solution, representing a strongly interacting and biologically relevant metal ion, and Pd 2+ complexes in acetonitrile (MeCN), a transition metal relevant for supramolecular chemistry in non-aqueous solvents. Ligand exchange in these complexes proceeds via different mechanisms, allowing us to investigate the capability of the MACE potentials to model structural and energetic features characteristic of both processes.