Establishing a commercially viable alloy from lab-scale alloy development research typically takes decades, in part due to the typical one-sample-at-a-time approach of sample synthesis, preparation, characterization, and analysis. To accelerate alloy development, we have designed an integrated, high-throughput method focused on parallelizing, miniaturizing, and automating each of these steps. In this method, alloys are selected using a CALPHAD-based alloy design algorithm. Then, test samples are built using laser metal deposition in 15-sample libraries with a unique geometry that facilitates rapid characterization. Samples can then be automatically prepared and characterized by XRD, EDS, and EBSD. These analyses, combined with various material property assessments, are then integrated with machine learning techniques to accelerate future materials analysis and guide subsequent composition and processing decisions. This method is applied to functionally graded metallic materials as well as discrete libraries of materials to better understand alloy development and improve predictive capabilities.