Consequently, independent molecular simulation studies on TX-100 aggregation reached different findings, pointing to spherical, as well as prolate, or oblate ellipsoidal micelles of different sizes, depending on the modelling technique used. Crucial aspects in MD studies can be addressed, e.g., by refining force fields or using enhanced sampling techniques. Even in these cases, however, quantitative agreement with experimental data is not guaranteed, due to issues associated with the possible formation of metastable states, intrinsic limitations of the models, or the need for prohibitively large computational power to, at least qualitatively, account for polydispersity. All these issues may be alleviated today by coupling molecular modelling to data-driven approaches. For example, Metainference is a Bayesian inference method that considers uncertainties regarding both the underlying model and the experimental errors, to produce bias forces that steer molecular dynamics simulations towards optimal agreement with experimental data. Metainference has been successfully employed in soft-matter systems targeting NOE, NMR, SAXS, and Cryo-EM data .