1. The foremost challenge is tackling the "black box" nature of complex machine learning models like deep neural networks. Why does a machine learning model make certain decisions? To this end, interpretability and explainability are paramount. Important developments have been made in the direction of symbolic regression, thereby discovering mathematical equations governing the complex phenomena characteristic of soft matter systems. Still, more effort is needed to gather further insight and intuition behind the underlying physics. 2. What makes soft-matter systems fascinating is also what makes them challenging: their multiscale nature. The aggregate effect of many small parts often sums up to large-scale supramolecular behavior-this emergent phenomenon is an outstanding challenge to effectively learn in ML models, and adequately generalize. This is the main reason why computer simulations remain essential nowadays and cannot easily be replaced by ML models alone. Looking to the future, the fusion of ML with physics-based simulation methods (e.g., molecular dynamics or Monte Carlo methods) is expected to have a strong impact. 3. Furthermore, navigating non-equilibrium dynamics stands as a colossal challenge. Almost all soft matter systems-including all of life-exist far from equilibrium. Worse, even systems that appear in equilibrium typically depend on non-equilibrium effects via their processing: the mere preparation (e.g., synthesis and subsequent treatment) impacts the final product . The absence of a well-established theory for non-equilibrium statistical mechanics can be an opportunity for inductive methods.