Capturing multiscale phenomena lies at the heart of soft-matter physics-from microscopic molecular architecture to mesoscopic structure, to macroscopic behavior. Limitations in the generalizability of ML models strongly limits the current prospects of replacing physics-based models. It is not so clear how extensive the training of an ML model ought to be to reproduce emergent phenomena, such as the selfassembly of soap bubbles from amphiphilic molecules. Coarse-grained modelling has been at the forefront of soft-matter simulations-it exploits scale separation to focus on the most relevant degrees of freedom. Advances in combining coarse-grained modelling with ML is key to further develop data-driven softmatter simulations. Much work is currently focused on ML-based coarse-grained potentials , ,