The use of synthetic data is also implicit in the derivation of tailored SAPT(DFT) potentials since they are parameterized from data on hypothetical monomers and dimers. Machine-learning models, by nature, require more data to train than could ever be provided by experimental sources, hence synthetic data are always used . Overall, investigations using synthetic reference data have predominantly centered on developing customised, system-specific force-field models, while little research has been performed on similar use of synthetic data in the development of potentials for HAIEFF-type models. In this context, some attempts to fit the empirical component to synthetic data sources include using energies calculated from the interaction of dimers , and more recently in the customisation of the empirical potential to solid-state DFT-D calculations on a specific system .