The proliferation of computational materials science has resulted in rapid prediction of energy materials, resulting in materials synthesis being a primary bottleneck in materials R&D. High throughput synthesis techniques can help address this bottleneck in certain settings, in particular where automation and parallelization can utilized in a manner that yields materials of sufficient quality and appropriate format for the target technology. Appropriate coupling to characterization and data science are also necessary to take advantage of the large material sets resulting from high throughput synthesis. Challenges in quality control and characterization of fundamental properties will be highlighted. The differences in synthesis design for bulk, surface and interface properties will be described using high throughput screening examples in the areas of light absorber, catalyst, and catalyst-absorber integration, respectively. The needs for continued development and increased utilization of data science will also be discussed.