We quantified the accuracy of the trained NNPs using the test set of the created DFT database, yielding energy and force root mean square errors (RMSE) within the training domain of 4.2 meV/atom and 51 eV/Å, respectively (see Table ). These errors are lower compared to NNPs that were also trained to model reactive events ( 5 meV/atom, 150 eV/Å) . In addition, we calculated the NNP (out-of-domain) errors for a randomly selected subset (5249 structures in total) taken from the basinhopping Monte Carlo simulations of germanium distributions (see below) for zeolite frameworks that were not part of the training database. We performed single-point calculations for this subset and obtained only mildly larger force errors for these test cases of about 70 eV/Å. However, the energy RMSE, which is more relevant in this work, which focusses on the Monte Carlo simulations sampling low-energy structures, is considerably lower (1.7 meV/atom) compared to the test set errors, demonstrating a good generalization capability of the NNPs (see Table ).