In the present context, using the EMD offers several advantages over other f-divergences. Notably, the EMD: (i) quantifies the difference between geoms distributions in interpretable units (Å, degrees), (ii) is well suited for comparison of multimodal distributions in this application case and (iii) it allows to correctly handle dihedrals distributions by using a periodic distance matrix. A scaling factor C is applied to EMD obtained for bonds distributions to allow comparison with EMD obtained for angles and dihedrals, since units are different. By default, we set C = 50, meaning that an EMD of 0.4 Å between bond distributions is equivalent to an EMD of 20 degrees between angle or dihedral distributions. To better penalize large mismatches between distributions and respect the weight of each geom, we do not normalize score components by the number of geoms defined in the topology. Therefore, it is important to note that the scores can be compared exclusively between trajectories of CG models generated in similar conditions (i.e., identical topology and non-bonded interactions parameters, but also simulation parameters) and with respect to a (wellsampled) reference AA-mapped trajectory. The components of the scoring function can be considered separately to exclusively evaluate the matching of bonds, angles or dihedrals distributions during the optimization procedure. Swarm-CG performs EMD calculations via PyEMD. 2.3. Iterative optimization procedure PSO is a population-based global optimization algorithm (aka metaheuristic) inspired by the collective movement of birds flocks and fish schools. In PSO, a swarm of individuals (referred to as "particles", each representing a set of values to be optimized) moves iteratively inside a bounded search space and cooperates to identify the best solution for a problem, according to an objective function. Usually, there are two groups of settings in PSO that control the cooperation within the swarm: (i) social attraction, which favors the collaboration among particles and (ii) cognitive attraction, which prompts a particle to rely on its individual experience. The swarm of particles can be initialized either randomly or from known approximate solutions. Metaheuristics such as PSO are particularly suited for solving black-box optimization problems and effectively handle noisy data.