While advances in machine learning over the past decade have made significant impacts in applications such as image classification, natural language processing and pattern recognition, scientific endeavors have only just begun to leverage this technology. This is most notable in processing large quantities of data from experiments. Research conducted at LLNL is the first to apply neural networks to the study of high-intensity short-pulse laser-plasma acceleration, specifically for ion acceleration from solid targets. While in most instances of neural networks they are used primarily for studying datasets, in this work the team uses them to explore sparsely sampled parameter space as a surrogate for a full simulation or experiment. Read more at LLNL News.