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About this Abstract
Meeting	MS&T22: Materials Science & Technology
Symposium	AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
Presentation Title	Physics-informed Machine Learning for Selected Area Electron Diffraction Data Analysis
Author(s)	Yu Lin, Nestor J. Zaluzec, Xiaoting Zhong, Jiadong Gong
On-Site Speaker (Planned)	Yu Lin
Abstract Scope	With the advancement of various electron probe scanning and manipulation techniques such as precession-based electron diffraction, compressive imaging, and hyperspectral imaging – including 4D-STEM, grain structure/orientation mapping has been made possible at nanoscale spatial resolution. This presents exciting opportunities for accelerated multidimensional data real-time processing with high levels of automation. We propose to use a deep learning-based approach for automatic crystal structure identification from the diffraction pattern images. We have demonstrated this concept using a simple type of diffraction data – single phase selected area electron diffraction (SAED) patterns. Deep learning models for automatic crystal structure identification in simulation SAED patterns have been developed by training on simulated data. We also proposed to couple highly relevant physics into ML models. A workflow to improve diffraction pattern analysis ML model performance by incorporating CALPHAD (i.e., CALculation of Phase Diagram) has been developed to improve the performance for phase/structure identification.

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