|About this Abstract
||2021 TMS Annual Meeting & Exhibition
||Characterization of Materials through High Resolution Imaging
||Adaptive Machine Learning for 3D Bragg Coherent Diffraction Imaging Reconstructions
||Alexander Scheinker, Reeju Pokharel
|On-Site Speaker (Planned)
We present an adaptive machine learning (ML) framework for the iterative phase retrieval problem faced by 3D coherent diffraction imaging (CDI) experiments. We combine 3D convolutional neural networks with adaptive model independent feedback which allows us to handle time varying systems for which ML methods alone are insufficient because their predictions drift if the system for which they were trained changes with time. For example, this regularly happens at CDI experimental beam lines due to changes in instrument setups and fluctuations of the light source. The proposed method is a novel robust approach to 3D reconstruction based on diffraction intensity measurements. We demonstrate the effectiveness of the method with both synthetic 3D structures and with measured complex crystal grain shapes.
||Machine Learning, Characterization, Modeling and Simulation