Note that the force field function parameters are traditionally thought to have a physical meaning, even when looked at individually (say, a dihedral stiffness or an atom size). When it comes to automated optimization of many parameters simultaneously, however, it can be argued that the physical meaning of individual parameters is blurred, and thus it make only limited sense to make such individual interpretations. Only the collection of parameters as a whole, to the extent they reproduce the targeted behaviour of the simulated system, has such a physical meaning. To emphasize this point, we call such automatically optimized force fields here gray-box potentials. Automated force field calibration has been done since the late 1990's 124 but has thus far mostly remained in the realm of small molecules; that said, also force fields for complex unstructured biomolecules, such as lipids, have been developed using automated approaches. Gradient-based minimizations, gradient-free methods such as Simplex 129-131 and evolutionary algorithms, surrogate or metamodel strategies, and their various combinations have been used for this purpose (Table ). In addition to the extreme complexity of the underlying optimization problem, one of the main hurdles preventing such automated approaches seems to be the computational cost arising from the fact that the optimization loop requires running MD simulations on (possibly thousands) of force field candidates. This cost can be alleviated, for example, by thermodynamic reweighing of cached MD trajectories. However, it appears that in some cases the gain from reweighing is smaller than the cost of trying the reweighing with insufficient data, 135 and that the reweighing only works reliably with minimal perturbations.