Codesign of programmable materials across length scales and disciplines

Programmable materials (i.e., materials whose properties can be deterministically programmed from their composition or microstructure) comprise a new horizon in materials engineering with transformative potential in areas ranging from soft robotics to biomedical engineering. Advances in high-resolution polymer additive manufacturing have enabled unprecedented control of material composition and architectural design in leading-edge programmable materials. However, progress has largely followed two parallel and weakly connected paths: approaches that emphasize geometric design of the material’s microstructure to tailor properties, and chemistry-driven approaches that focus on developing functional printable polymeric materials. This separation has constrained the exploration of the coupled effects of material composition and structure that are essential for true programmability. Here, we present an overview of the advances in both research tracks and propose a perspective on achieving codesign of material chemistry and architecture across multiple length scales, for next-generation programmable polymeric materials. We discuss emerging data-driven and automated design strategies, including self-driving laboratories, high-throughput experimentation, and machine learning, as a route toward a scalable pathway to navigate the vast and highly coupled design space of programmable polymeric materials. By unifying materials chemistry, architectural design, and data-driven discovery, this perspective outlines a framework for accelerating the discovery of truly programmable matter.