The research concerns diffraction-based methods for characterizing thin, 2D materials, specifically using position averaged convergent beam electron diffraction (PACBED). This is a relatively new technique for characterizing materials and in great need of method development concerning which methods to use and how to optimize experimental parameters for the best parameter prediction, etc. We have almost finished a project in this direction within recently finished NAISS compute and storage projects, where we predict phase (crystal structure) and thickness (number of atomic layers). The project concerns both regression and classification utilizing convolutional neural networks in PyTorch. We are investigating predictions for many different experimental/simulation parameters to optimize the method, resulting in heavy computations both from a data generation (performed on other resources) and an ML training standpoint. This part also requires substantial storage. We aim to finalize this work within this proposed 2 month compute and storage project. The project involves both experimental specialists from the Eva Olsson group at Physics at Chalmers, and AI expertise from the Mathematical Sciences and Electrical Engineering departments.