Automated Classification of Big X-ray Diffraction Data Based on Machine Learning and Deep Learning Techniques

Abstract

X-ray diffraction (XRD) data analysis is a very important technique in materials science. However, traditional methods are time-consuming, require human expertise, and are prone to errors due to the large and complex datasets. This study explores the potential of machine learning (ML) and deep learning (DL) techniques to automate the classification of large X-ray diffraction datasets. This work provides a historical overview of X-ray diffraction, how it occurs, and the fields it involves. It also presents the contributions of artificial intelligence (AI) fields, including machine learning and deep learning, to improve classification and prediction material. The study attempts to present the best of what researchers have achieved in this field, providing a scalable solution for high-throughput materials characterization and its potential for advancing automated XRD data interpretation in both research and industrial applications.