Though the penetration of ML in soft matter has been lagging against hard condensed matter, more recent developments show that the outstanding challenges faced by conformational flexibility (i.e., the role of entropy) are increasingly being addressed. In accordance with other fields of physics, chemistry, and materials science, the pursuit of stronger inductive bias (i.e., building physics in the model) systematically helps build better models in an area that is notoriously scarce in data-experimental or from computer simulations. The continued development of ML techniques for soft-matter physics, and the cross-penetration of ML with multiscale modelling, is helping push soft-matter physics toward higherprecision predictive modelling, soft-materials design and optimization, and reproducing entire experiments on the computer .