Random forest prediction of crystal structure from electron diffraction patterns incorporating multiple scattering

Diffraction is the most common method for solving unknown crystal structures, but nanoscale heterogeneity poses substantial challenges. Here we train random forest models to predict the crystal system, space group, and lattice parameters from one or more unknown 2D electron diffraction patterns. We apply this architecture to a 4D-STEM scan of over 100 gold nanoparticles, accurately predicting the crystal structure and lattice constants. Our ML architecture significantly accelerates the analysis of electron diffraction patterns, particularly in the case of unknown crystal structures at speeds amenable to live analysis. This work also releases ~360 million simulated 2D dynamical electron diffraction patterns from 36,000 materials, each with 100 unique crystal orientations and 100 specimen thicknesses.