Machine learning accelerates STEM simulations

Our paper on using machine learning to develop fast, approximate methods for STEM image simulations has been published. The method requires training on full multislice simulations, but once its trained for a particular system, it’s orders of magnitude faster to execute. The code and data are also available. Of particular note, the first author on this paper was a undergraduate, Aidan Combs. Congratulations, Aidan!