Coarse-grained modeling enables the simulation of systems on length and time scales that are orders of magnitude larger than those accessible in traditional all-atom simulations. By adopting a low-resolution representation of molecules and computationally efficient interaction potentials, coarse-graining is associated with a smoothening of the molecular free-energy landscape. Due to the contraction of the phase-space, dynamics of coarse-grained models is intrinsically accelerated, providing additional speedup and faster statistical sampling over all-atom simulations, beyond the increased speed due to lower-resolution molecular representation and fast effective interaction potentials, 3 even though this comes at the cost of a generally poor description of dynamical properties, such as diffusion coefficients, unless explicitly accounted for. Acceleration in the conformational sampling enables, for example, the straightforward study of phase separation, like the self-assembly of lipid bilayers, or phase transitions, like spontaneous ice nucleation. Particle-field models represent the interactions between different molecules through their density fields, leading to a particularly high level of smoothening of the free-energy surface and hence a correspondingly large acceleration of the dynamics. In fact, we reported that particle-field molecular dynamics models enable (sub-)nanosecond equilibration of dispersed charged surfactants to fully self-assembled structures even in the presence of large activation energies that would significantly lengthen the characteristic self-assembling times even using particularly successful CG models like the MARTINI. As such, particle-field approaches provide a powerful tool for studying processes that are computationally inaccessible by standard molecular modeling.