Using the NLP loss function shifts the most balanced inter-domain weighting factor distribution to a setup with ∼ 25 % of the overall weight assigned to the density (see figures 6 and 7 as well as tables 6, 7, 8 and 9 in section 5). The NLP works as expected and improves the comparability of the errors (i.e. the loss function values) of the different target property domains. Furthermore, the best optimization result is achieved by using the NLP loss function. However, this method introduces an additional layer of complexity represented by the sensitivity hyperparameter c. It needs to be determined and configured for every optimization objective separately and requires a deeper insight into that specific optimization objective. This might not be a problem for experienced users but is oppositional to a user-friendly, fast and automatized force field optimization workflow.