The approach is very general and has the advantage over bottom-up coarse-graining approaches in that it can incorporate both experimental and all-atom data, as exemplified in a recent work, where the loss function to parameterize dipalmitoyl-phosphatidylcholine (DPPC) phospholipid bilayers included both the computationally-determined density profiles and the experimental value of the area-per-lipid. BO provides a state-of-the-art algorithm for optimizing costly fitness functions without access to gradients in low-dimensional parameter spaces. However, this approach becomes problematic when the number of particle species S increases, resulting in a large S(S -1)/2-dimensional parameter space, or, even worse when also including intramolecular bonded parameters into the optimization problem. Optimization techniques employing gradients generally achieve better convergence and work well on higher-dimensional spaces; therefore, they are better suited for force field parameterization.