Conference Tools for 2025 TMS Annual Meeting & Exhibition

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About this Abstract
Meeting	2025 TMS Annual Meeting & Exhibition
Symposium	Characterization of Materials through High Resolution Coherent Imaging
Presentation Title	AI-Driven Workflow for Autonomous High-Resolution Scanning X-Ray Microscopy
Author(s)	Tao Zhou, Saugat Kandel, Mathew Cherukara, Charadatta Phatak, Martin Holt
On-Site Speaker (Planned)	Tao Zhou
Abstract Scope	We report the Fast Autonomous Scanning Toolkit (FAST) that combines 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. FAST requires no prior information about the sample and is computationally efficient. We demonstrate FAST in a dark-field X-ray microscopy experiment on an exfoliated multilayer WSe2 thin film. Our studies show that a FAST scan of <25% is sufficient to accurately image all the spontaneously formed bubbles in the sample, with a decision making time of less than 1 second per 50 points. 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.
Proceedings Inclusion?	Planned:
Keywords	Machine Learning, Characterization,

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