As DiffCG has been successfully demonstrated to optimize CG models with transferability over a wide range of temperatures, it is reasonable to expect that our approach is able to construct CG models transferable across other conditions, such as interfaces, multicomponent systems, diverse compositions, and so on. Extending this methodology to handle such intricate environments may necessitate the development of CG potentials with improved expressiveness. Recent breakthroughs in machine-learning force-fields (MLFF) hold great promise as a viable approach for developing accurate and transferable CG models. In particular, MLFFs have shown their effectiveness in CG simulations of various systems, including organic molecules, 27 liquids, and fast-folding proteins. However, the extent to which MLFFs can be applied to condensed-phase macromolecular systems remains an intriguing question for further exploration. Furthermore, an ideal scenario for the development of CG models envisions integration with an automated optimization loop, possibly through active learning schemes, as done in MLFFs of interatomic potential. This integration would unlock the full potential of coarse-graining techniques, enabling the modeling of complex systems across extensive spatio-temporal scales. DiffCG is anticipated to be a transformative tool in structural coarse-graining, with the potential to address a wide range of challenges and pave the way for innovative developments in CG modeling.