Spall experiments require loading facilities capable of generating extreme strain rates in materials of interest. These facilities generally require large investments towards obtaining a single experimental dataset and are typically by nature low throughput. Laser driven micro-flyer plate experiments to generate spall failure provide an alternative lab-bench scale experimental methodology. Recent developments in this space have resulted in experimental systems capable of generating hundreds of impact shots per day. In this study, a curated process is carried out to identify and automate the bottleneck aspects of experimental methodology, potentially accelerating the pace of conducting experiments by orders of magnitude. This enables statistical exploration of the material space under extreme strain rate events. Specimen handling, positioning, laser firing and data analysis aspect of the process are automated. This high throughput experimental process organically integrates with parallel aspects of large data management and machine learning to develop a large-scale high throughput experimental and analysis frame work.