# Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data

arXiv: https://arxiv.org/abs/2202.10983 · HTML: https://mlsanity.com/paper/2202.10983/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 55 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Instance-aware Semantic Segmentation via Multi-task Network Cascades](https://mlsanity.com/paper/1512.04412/) (2015)
- [You Only Learn One Representation: Unified Network for Multiple Tasks](https://mlsanity.com/paper/2105.04206/) (2021)
- [BraggNN: Fast X-ray Bragg Peak Analysis Using Deep Learning](https://mlsanity.com/paper/2008.08198/) (2020)
- [Machine learning applied to single-shot x-ray diagnostics in an XFEL](https://mlsanity.com/paper/1610.03378/) (2016)
- [X-ray Scattering Image Classification Using Deep Learning](https://mlsanity.com/paper/1611.03313/) (2016)
- [Fast Fitting of Reflectivity Data of Growing Thin Films Using Neural Networks](https://mlsanity.com/paper/1910.02898/) (2019)
- [Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images](https://mlsanity.com/paper/2009.02256/) (2020)

## Built on by (most cited first, 2 of 2)

- [Amortized Bayesian Inference of GISAXS Data with Normalizing Flows](https://mlsanity.com/paper/2210.01543/) (2022)
- [Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders](https://mlsanity.com/paper/2606.14999/) (2026)

## Abstract (arXiv)

Understanding the processes of perovskite crystallization is essential for improving the properties of organic solar cells. In situ real-time grazing-incidence X-ray diffraction (GIXD) is a key technique for this task, but it produces large amounts of data, frequently exceeding the capabilities of traditional data processing methods. We propose an automated pipeline for the analysis of GIXD images, based on the Faster R-CNN deep learning architecture for object detection, modified to conform to the specifics of the scattering data. The model exhibits high accuracy in detecting diffraction features on noisy patterns with various experimental artifacts. We demonstrate our method on real-time tracking of organic-inorganic perovskite structure crystallization and test it on two applications: 1. the automated phase identification and unit-cell determination of two coexisting phases of Ruddlesden-Popper 2D perovskites, and 2. the fast tracking of MAPbI3 perovskite formation. By design, our approach is equally suitable for other crystalline thin-film materials.