Soft-matter science has gone through substantial evolution in the last half century. In polymer physics, experiments and theory have worked hand in hand early on to measure coveted critical exponents, and link to general statistical mechanics theory. Computer simulations have played an increasingly important role-they offer invaluable microscopic detail and reach out to ever-growing system sizes , . They combine basic generic concepts with specific material properties. In the last decade, data-driven methods, and more recently machine learning (ML), have become increasingly popular in soft matter. They offer an inductive approach to help bridge the scales, and more broadly solve complex structure-property relationships.