We can now envision a broad use of these instruments to study not only static systems but also multi-level studies of the dynamic evolution of materials with time, temperature, or other parameters, even in situ or operando 6. Ongoing advances in instrumentation, such as the development of next-generation X-ray and electron detectors 4, 5, have meant that scanning microscopes can now image faster and at higher resolutions than ever before. Notwithstanding the variation in the probe modalities, these instruments all rely on a scan of the sample to generate spatially resolved signals that are then collected to form an image of the sample. Scanning microscopes are versatile instruments that use photons, electrons, ions, neutrons, or mechanical probes to interrogate atomic-scale composition, topography, and functionality of materials, with up to sub-atomic spatial resolution and sub-picosecond time resolution 1, 2, 3. FAST is easy to adapt for any scanning microscope its broad adoption will empower general multi-level studies of materials evolution with respect to time, temperature, or other parameters. Our studies show that a FAST scan of <25% is sufficient to accurately image and analyze the sample. We test FAST in simulations and a dark-field X-ray microscopy experiment of a WSe 2 film. FAST requires no prior information about the sample, is computationally efficient, and uses generic hardware controls with minimal experiment-specific wrapping. We report the Fast Autonomous Scanning Toolkit (FAST) that addresses this challenge by combining a neural network, route optimization, and efficient hardware controls to enable a self-driving experiment that actively identifies and measures a sparse but representative data subset in lieu of the full dataset. Modern scanning microscopes can image materials with up to sub-atomic spatial and sub-picosecond time resolutions, but these capabilities come with large volumes of data, which can be difficult to store and analyze.
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