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Machine learning for classifying and interpreting coherent X-ray speckle patterns

arXiv 2211.08194 ↗First posted 15 Nov 2022cond-mat.mtrl-sci cs.CV cs.LGcited by 0

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Abstract (arXiv)

Speckle patterns produced by coherent X-ray have a close relationship with the internal structure of materials but quantitative inversion of the relationship to determine structure from speckle patterns is challenging. Here, we investigate the link between coherent X-ray speckle patterns and sample structures using a model 2D disk system and explore the ability of machine learning to learn aspects of the relationship. Specifically, we train a deep neural network to classify the coherent X-ray speckle patterns according to the disk number density in the corresponding structure. It is demonstrated that the classification system is accurate for both non-disperse and disperse size distributions.

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References · 20

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8 more
  1. 2021Noise Reduction in X-ray Photon Correlation Spectroscopy with Convolutional Neural Networks Encoder-Decoder Modelscited by 5
  2. 2018Interpretable deep learning for guided structure-property explorations in photovoltaicscited by 3
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  8. 2020BLAST: Bridging Length/time scales via Atomistic Simulation Toolkitcited by 1

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