At the same time, the swarm of (possibly thousands) of force field candidates created as a side product of the optimisation loop is not just computational waste but high-quality raw material that can be upcycled to train machine learning algorithms (such as a neural network), to act as a surrogate for the costly MD simulation in probing the optimization landscape, or to create an expert (machine learning) model that directly predicts force field parameters from experimental data. Additionally, the major speed up by the next generation supercomputing architectures 136 may accelerate further automated parameterization procedures.