However, these hyperparameters are often 'baked in' to the design of the force field, and correcting known problems can be challenging. Until recently, the manual training of force field parameters for a given set of hyperparameters (for example, Lennard-Jones parameters for a given charge model) would be extremely time-consuming. However, allied with improvements in data collection and retrieval, advances in automated parameter fitting to experimental liquid properties now make this possible. Most notably, the ForceBalance software enables reproducible and automated force field parameterisation against a set of target data that can include either QM or experimental input data. In the context of the current work, ForceBalance was used to tune the QM-to-MM mapping parameters against experimental liquid data in the aforementioned study by Kantonen et al, and was also used to rapidly re-fit Lennard-Jones parameters for a range of proposed implicit solvent models (hyperparameters) in the development of the RESP2 method. An automated toolkit for force field design, training and testing.