Using the Genetic Algorithm to Determine the Parameters of X-Ray Diffractograms Through Different Functions

Abstract

X-ray diffraction is an important technique used to determine crystallite size in materials science. However, determining the crystallite size accurately from X-ray diffraction data can be challenging due to the peak broadening and shape complexities. In this study, a genetic algorithm (GA) is employed to optimize the determination of crystallite size using X-ray diffraction data. The GA is used to fit a combination of Gaussian, Lorentzian, and Voigt functions to the X-ray diffraction data and determine the optimal crystal size. The optimized crystallite size is found to be consistent with other characterization techniques, demonstrating the validity of the proposed GA approach. Overall, this study presents a novel approach for the determination of crystallite size from X-ray diffraction data using GA optimization, which can be widely used in the field of materials science for accurate and efficient characterization of crystalline materials.