The quality and completeness of the information embedded in the training set directly conditions the accuracy of the output FF, as well as its ability to generalize to different types of lipids and to different thermodynamic conditions (than those included in the training set). In this study, because the bonded parameters are defined as general building blocks and redundant across lipid types (Fig. ), while at the same time the space of their potential solutions is restrained by other simulation parameters remaining constant (notably non-bonded parameters set to Martini 3.0.0 4 ), using a rich training set can be expected to output a consistent CG lipid FF that generalizes well to other types of lipids and to other observables than those used as objectives during optimization . The CG representations of the lipids, however, might still limit the extent to which reference data can be fitted, depending on the degrees of freedom preserved by the molecular models (i.e. depending on the choice of beads describing non-bonded interactions, their reference positioning, the topology of the CG models and the potentials used to describe bonded interactions).