ISSN : 2583-939X
Duy Sang Nguyen and Ha Hoang Quoc Thi
World Jour. of Phys., 2 (1):161-172
Duy Sang Nguyen : School of Education, Can Tho University, Can Tho City 94000, Vietnam
Ha Hoang Quoc Thi : School of Education, Can Tho University, Can Tho City 94000, Vietnam
DOI: https://doi.org/10.56439/WJP/2024.1115
Article History: Received on: 17-Jan-24, Accepted on: 05-Apr-24, Published on: 30-Apr-24
Corresponding Author: Duy Sang Nguyen
Email: ndsang@ctu.edu.vn
Citation: Duy Sang Nguyen (2024). Prediction of crystal size and microstrain using artificial neural network from Gaussian peak shape analysis of X-ray diffraction data. World Jour. of Phys., 2 (1 ):161-172
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.