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

arXiv: https://arxiv.org/abs/2211.08194 · HTML: https://mlsanity.com/paper/2211.08194/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 64 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
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- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Understanding Neural Networks Through Deep Visualization](https://mlsanity.com/paper/1506.06579/) (2015)
- [The Numerics of Phase Retrieval](https://mlsanity.com/paper/2004.05788/) (2020)
- [Three-dimensional Coherent X-ray Diffraction Imaging via Deep Convolutional Neural Networks](https://mlsanity.com/paper/2103.00001/) (2021)
- [A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations](https://mlsanity.com/paper/2108.08882/) (2021)
- [Quantitative Digital Microscopy with Deep Learning](https://mlsanity.com/paper/2010.08260/) (2020)
- [Multi defect detection and analysis of electron microscopy images with deep learning](https://mlsanity.com/paper/2108.08883/) (2021)
- [Adaptive 3D convolutional neural network-based reconstruction method for 3D coherent diffraction imaging](https://mlsanity.com/paper/2008.10094/) (2020)
- [Noise Reduction in X-ray Photon Correlation Spectroscopy with Convolutional Neural Networks Encoder-Decoder Models](https://mlsanity.com/paper/2102.03877/) (2021)
- [Interpretable deep learning for guided structure-property explorations in photovoltaics](https://mlsanity.com/paper/1811.06067/) (2018)
- [Assessing Graph-based Deep Learning Models for Predicting Flash Point](https://mlsanity.com/paper/2002.11315/) (2020)
- [Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychography](https://mlsanity.com/paper/2203.15413/) (2022)
- [Phase retrieval for Bragg coherent diffraction imaging at high X-ray energies](https://mlsanity.com/paper/1811.06181/) (2018)
- [A Deep Learning Approach for Characterizing Major Galaxy Mergers](https://mlsanity.com/paper/2102.05182/) (2021)
- [Supervised Classification Methods for Flash X-ray single particle diffraction Imaging](https://mlsanity.com/paper/1810.10786/) (2018)
- [BLAST: Bridging Length/time scales via Atomistic Simulation Toolkit](https://mlsanity.com/paper/2002.10401/) (2020)

## 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.