In conclusion, the black-box ML potentials and the automated parameterization of classical force fields to create gray-box potentials are promising tools for improving MD models, e.g., by introducing more accurate water models and including electronic polarization effects, improve thus their consistency with experiments, thus paving the way toward more realistic MD simulations of membranes and other biomolecular systems. However, although black-box ML potentials can reach QM-like precision, and methods like gradient-based optimization have consistently shown their advantages in improving the classical force fields, no single approach has yet emerged as clearly superior to tackle this task. For this to happen, practitioners need to keep pushing the applications towards larger and more complex sys- tems. Considering this, we expect that the full potential of these approaches and the most successful methodologies of complex biomolecules will only be revealed in the coming years.