Prediction of Crystal Size and Microstrain Using Artificial Neural Network From Gaussian Peak Shape Analysis of X-Ray Diffraction Data

Abstract

X-ray diffraction (XRD) is a widely used technique in materials science to determine crystal structure, crystal size and peak shape of crystalline materials. However, the interpretation of XRD data is often challenging due to the complexity of the diffraction patterns and the presence of noise. In this study, we demonstrate the application of artificial neural networks (ANNs) to predict crystal size and peak shape from XRD data using the Gaussian function. ANNs are a powerful machine learning tool that can learn complex relationships between input and output variables. Our results suggest that ANNs can be a valuable tool for the interpretation of XRD data, especially when the diffraction patterns are complex or noisy. The average value of the crystal size is estimated and evaluated by the figure of merit parameter. This approach has potential applications in materials science, where accurate characterization of crystal structure and size is essential for understanding material properties and designing new materials.