The loss function, to be minimized, reformulates our many-objective optimization problem into a singleobjective one by aggregating the distances from both top-down and bottom-up objectives into a global "FF accuracy score" (loss global in Fig. and Fig. ). Fitting the APL and Dʜʜ experimental data is formulated as the two primary objectives, having equal importance (top-down global features), while reproducing within the CG models the distributions of bond and angle values calculated from AA MD simulations constitutes a secondary objective (bottom-up local features). To this end, we apply a soft penalty which role is to ensure large deviations from the reference bottom-up data are forbidden and allows to effectively distribute in between the bonded building blocks the residual error inherent to the coarsegraining process (i.e. inherent to the reduction of the number of degrees of freedom in between AA and CG models). The loss function is defined as