# Identification of Crystal Symmetry from Noisy Diffraction Patterns by A Shape Analysis and Deep Learning

arXiv: https://arxiv.org/abs/2005.12476 · HTML: https://mlsanity.com/paper/2005.12476/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 6 of 6)

- [Ab Initio Structure Solutions from Nanocrystalline Powder Diffraction Data](https://mlsanity.com/paper/2406.10796/) (2024)
- [Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns](https://mlsanity.com/paper/2202.00204/) (2022)
- [Towards End-to-End Structure Solutions from Information-Compromised Diffraction Data via Generative Deep Learning](https://mlsanity.com/paper/2312.15136/) (2023)
- [Predicting failure characteristics of structural materials via deep learning based on nondestructive void topology](https://mlsanity.com/paper/2205.09075/) (2022)
- [Direct Motif Extraction from High Resolution Crystalline STEM Images](https://mlsanity.com/paper/2303.07438/) (2023)
- [Towards Space Group Determination from EBSD Patterns: The Role of Deep Learning and High-throughput Dynamical Simulations](https://mlsanity.com/paper/2504.21331/) (2025)

## Abstract (arXiv)

The robust and automated determination of crystal symmetry is of utmost importance in material characterization and analysis. Recent studies have shown that deep learning (DL) methods can effectively reveal the correlations between X-ray or electron-beam diffraction patterns and crystal symmetry. Despite their promise, most of these studies have been limited to identifying relatively few classes into which a target material may be grouped. On the other hand, the DL-based identification of crystal symmetry suffers from a drastic drop in accuracy for problems involving classification into tens or hundreds of symmetry classes (e.g., up to 230 space groups), severely limiting its practical usage. Here, we demonstrate that a combined approach of shaping diffraction patterns and implementing them in a multistream DenseNet (MSDN) substantially improves the accuracy of classification. Even with an imbalanced dataset of 108,658 individual crystals sampled from 72 space groups, our model achieves 80.2% space group classification accuracy, outperforming conventional benchmark models by 17-27 percentage points (%p). The enhancement can be largely attributed to the pattern shaping strategy, through which the subtle changes in patterns between symmetrically close crystal systems (e.g., monoclinic vs. orthorhombic or trigonal vs. hexagonal) are well differentiated. We additionally find that the novel MSDN architecture is advantageous for capturing patterns in a richer but less redundant manner relative to conventional convolutional neural networks. The newly proposed protocols in regard to both input descriptor processing and DL architecture enable accurate space group classification and thus improve the practical usage of the DL approach in crystal symmetry identification.