Machine Learning for Ultrafast Electron Diffraction

Ultrafast electron diffraction (UED) experiments provide high-quality data about atomic structure and dynamics of functional materials down to fs-ps timescales. Imminent improvements in repetition rate of electron sources and experimental facilities will dramatically increase the size of this available data and will provide new capabilities for ultrafast science. These advances will require analysis techniques that can efficiently extract atomistic insights from raw diffraction images. In this talk, I will describe a deep generative model, trained on existing ultrafast electron diffraction data on photoexcited two-dimensional and layered materials obtained at SLAC, as well as trajectories from classical molecular dynamics and non-adiabatic quantum molecular dynamics simulations. The model is used to analyze lattice distortions, phonon modes and changes in local crystal structure due to photoexcitation and identify potential precursors to structural phase transformations in these materials. Extensions to the model to utilize streaming data for real-time analysis and the utility of the model in experimental design will also be discussed.