This situation changed with the development of extremely fast parametrized quantum chemical methods (TD-DFTB) and the advent of machine learning potentials. Now, computational overheads may have a significant impact on the costs. To tackle this issue, we are developing Newton-X NS, which aims at extreme computational efficiency and better data management standards. Newton-X NS will be presented in a separate paper in the future, but we will highlight some features here.