In this regard, differentiable molecular dynamics, leveraging advancements in GPU-based hardware, highly efficient implementations of automatic differentiation (autodiff), and a generalized back-propagation algorithm, is becoming increasingly popular. Differentiable molecular dynamics can be used to optimize force field parameters through the dynamical evolution of the systems under study. A conventional machine-learning-based approach consists in training machine learning potentials, often based on artificial neural networks (NN), on high-accuracy data (for example, either DFT calculation datasets or all-atom simulations for CG modeling). This methodology has been successfully employed to determine NN pair potentials for CG water models that replicate radial and angular distribution functions, a NN potential for a CG model of chignolin and the parameterization of a CG protein force field, where the parameters are trained to minimize the root-mean-square deviation (RMSD) from the native state structures. However, differential molecular dynamics is not limited to training NN potentials; in principle, it can also be applied to the optimization of known analytical functions, like those that are still commonly used in molecular simulations.