X-ray diffraction pattern analysis assisted by convolutional neural networks
TL;DR
This work represents a fully automated pipeline integrating data acquisition, diffractogram generation, image transformation, and large-scale compound identification, highlighting the dependence of the approach on the selected CNN architecture.
Abstract
ABSTRACT This work presents an automated methodology for identifying inorganic compounds from powder X-ray diffraction (XRD) patterns using convolutional neural networks (CNNs). Crystallographic information files (CIFs) were automatically retrieved from the Crystallography Open Database (COD) and processed to generate theoretical diffractograms, including peak positions and intensities derived from structure factor calculations. A total of 33,180 inorganic compounds were processed. To enable CNN-based classification, diffraction patterns were transformed into RGB images using the DeepInsight approach combined with t-distributed stochastic neighbor embedding (t-SNE), allowing the homogenization of feature dimensionality across the dataset. The resulting images were used to train a convolutional neural network for feature extraction, followed by fully connected artificial neural network layers for multiclass classification. The proposed model achieved an average classification accuracy of 69% across 33,180 classes. Compared to traditional search–match approaches, this method reduces computational complexity and eliminates the need for manual interpretation of diffractograms. This work represents a fully automated pipeline integrating data acquisition, diffractogram generation, image transformation, and large-scale compound identification. The results demonstrate a modest yet promising performance, highlighting the dependence of the approach on the selected CNN architecture.