Unveiling the Crystal Lattice: AI-Enhanced XRD Analysis

"Unveiling the Crystal Lattice: AI-Enhanced XRD Analysis" delves into the revolutionary fusion of X-ray diffraction (XRD) techniques with artificial intelligence (AI) algorithms. As XRD remains a cornerstone for materials characterization, the book explores how the integration of AI enhances the accuracy, speed, and efficiency of XRD analysis, opening up new frontiers for research and industry applications.The book begins by establishing a solid foundation in XRD analysis, highlighting its importance, historical context, and challenges. It then introduces the concept of AI and its potential impact on XRD analysis. From there, it delves into the fundamentals of X-ray diffraction, providing readers with a comprehensive understanding of the technique.Various traditional XRD analysis techniques, including qualitative and quantitative analysis, texture analysis, and residual stress analysis, are explored. The book then delves into machine learning and its application in XRD analysis, covering data preprocessing, feature selection, classification algorithms, regression algorithms, and the use of neural networks and deep learning.

Autorentext

Mme C. Arathi est professeur adjoint de physique au département des sciences fondamentales du GNITS. Elle a obtenu sa maîtrise en physique de l'état solide à l'Université d'Osmania en 2009. Elle a 14 ans d'expérience dans l'enseignement. Ses recherches portent sur la physique de l'état solide, les sciences des matériaux, la physique informatique et la physique quantique.

Klappentext

"Unveiling the Crystal Lattice: AI-Enhanced XRD Analysis" delves into the revolutionary fusion of X-ray diffraction (XRD) techniques with artificial intelligence (AI) algorithms. As XRD remains a cornerstone for materials characterization, the book explores how the integration of AI enhances the accuracy, speed, and efficiency of XRD analysis, opening up new frontiers for research and industry applications.The book begins by establishing a solid foundation in XRD analysis, highlighting its importance, historical context, and challenges. It then introduces the concept of AI and its potential impact on XRD analysis. From there, it delves into the fundamentals of X-ray diffraction, providing readers with a comprehensive understanding of the technique.Various traditional XRD analysis techniques, including qualitative and quantitative analysis, texture analysis, and residual stress analysis, are explored. The book then delves into machine learning and its application in XRD analysis, covering data preprocessing, feature selection, classification algorithms, regression algorithms, and the use of neural networks and deep learning.