Powders used in cold-spray can contain hundreds of thousands of particles in just a fifty gram sample. This brings our flowability dataset into the domain of Big Data. In order to build powerful machine learning models a lot of computational resources are needed. Our team has received a grant for access to the XSEDE Supercomputer. This access allows our team to deploy analysis on some of the fastest computers in the world.

The Extreme Science and Engineering Discovery Environment (XSEDE) was given access to our team from the NSF on June 30th, 2020 with an annual access grant. These computers run up to 16 NVIDIA Tesla V100 GPUs and up to 48 core Intel Xeon CPUs. These resources will be used to train models and deploy methods such as Sequential Auto-Encoders, Convolutional Neural Networks and Virtual Powder Generation. This research is exploring the frontier of the cross-section of Material Science and Data Science.