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In-situ Electron Energy Loss Spectroscopy (EELS) is an instrumental techniquethat has traditionally been used to understand how the choice of materialsprocessing has the ability to change local structure and composition. However,more recent advances to observe and react to transient changes occurring at theultrafast timescales that are now possible with EELS and Transmission ElectronMicroscopy (TEM) will require new frameworks for characterization and analysis.We describe a machine learning (ML) framework for the rapid assessment andcharacterization of in operando EELS Spectrum Images (EELS-SI) without the needfor many labeled training datapoints as typically required for deep learningclassification methods. By embedding computationally generated structures andexperimental datasets into an equivalent latent space through VariationalAutoencoders (VAE), we effectively predict the structural changes at latencyscales relevant to closed-loop processing within the TEM. The frameworkdescribed in this study is a critical step in enabling automated, on-the-flysynthesis and characterization which will greatly advance capabilities formaterials discovery and precision engineering of functional materials at theatomic scale.

Embedding theory in ML toward real-time tracking of structural dynamics through hyperspectral datasets