Polymers are essential to a wide range of technologies, yet designing them with targeted structural and functional properties remains a grand challenge. A major opportunity lies in applying machine learning to help navigate the vast combinatorial design space—spanning sequence, composition, architecture, morphology, processing, and more—to discover new formulations or replace existing ones with more sustainable alternatives. However, this complexity, combined with data scarcity and characterization challenges, limits the effectiveness of purely rational design and/or high-throughput screening. In this talk, I will describe some of our recent efforts to integrate molecular simulation, machine learning, and theory to map and navigate structure–function relationships in chemically and topologically diverse polymeric materials. I will describe strategies that we have employed across a range of applications to overcome data limitations in polymer science by developing physics-informed (or guided) models and exploring other algorithmic innovations. A focal example will examine how we can design complex polymer additives that tune material rheology, with a particular focus on shear-thinning fluids. This example, along with other case studies, will showcase utility, limitations, and opportunities for data-driven approaches in modern-day science; and how coupling them with (or using them to develop) physical insight can accelerate innovation and deepen materials understanding.