A Neural Network‐Based Numerical Approach for X‐Ray Diffraction Analysis: Application to Spinel Materials

ABSTRACT This work presents a neural‐network‐based numerical approach for analyzing x‐ray diffraction (XRD) patterns of spinel materials. The open‐source CERN ROOT toolkit was used to implement the workflow. Four multilayer perceptron (MLP) neural networks were designed to predict key structural parameters: the lattice parameter, crystallite size, oxygen position and inversion degree. For each CoAl 2 O 4 and MgAl 2 O 4 spinel, the networks were trained using artificial spectra generated from a single CIF file, based on a theoretical framework describing the XRD response. Training was completed within a reasonable time for all MLPs and prediction is instantaneous once the models are trained. Single‐phase spinel samples were considered in this study. A ROOT coded procedure was developed to remove background, identify diffraction peaks and assign Miller indices in experimental spectra. The trained MLPs achieved prediction accuracies of ~95% for the lattice parameter, crystallite size and oxygen position and ~80% for the inversion degree. Validation on experimental XRD data of CoAl 2 O 4 and MgAl 2 O 4 showed good agreement with Rietveld refinement results and with values reported in the literature, while offering a substantial reduction in computation time thanks to the instantaneous prediction capability of the trained MLP models.