This talk will discuss strategies to implement an accelerated discovery and codesign platform for efficient design and discovery of new ferroelectric materials and their properties in relevant devices. Achieving this goal requires moving beyond conventional, linear approaches to materials discovery, transforming them into a cyclic and iterative process integrating computation, experiment, and theory to formulate the processing-structure-property-performance relationships necessary to advance ferroelectric materials and devices. We will describe our progress in using machine learning to automate and accelerate materials characterization leading to adaptive learning for simulation and high-throughput synthesis and characterization.