Automated force field development can be roughly divided into (1) approaches where a machine learning algorithm, such as a neural network 113-116 or a kernel-based ap-proach 117 acts as the force field, predicting the forces for a specific configuration of molecules (black-box potentials), and (2) approaches where automated optimization algorithms (including ML tools 118 ) are used to find better parameters for existing force field functions (gray-box potentials). Quality-evaluated open access databanks of MD trajectories can aid both of these approaches, as they guide the choice of optimization targets (by pinpointing the typical failures of existing MD force fields), establish quality standards, and can even provide training data for ML methods in force field parameterization.