Advances in artificial intelligence-based approaches to enhance dark field X-ray microscopy analysis

Area of Science:

• Materials Science
• Condensed Matter Physics
• Data Science

Background:

• Dark field X-ray microscopy (DFXM) offers non-destructive analysis of dislocations in bulk materials.
• Interpreting complex DFXM data presents significant analytical challenges.
• Traditional electron microscopy methods have limitations in penetration depth and sample preparation.

Purpose of the Study:

• To review advances in using artificial intelligence (AI) to enhance DFXM data analysis.
• To focus on AI-driven characterization of dislocations in crystalline materials.
• To demonstrate physics-informed AI approaches for improved DFXM interpretation.

Main Methods:

• Development of physics-informed AI algorithms combining theoretical understanding and data science.
• Application of wavelet transforms and Bayesian inference for data analysis.
• Creation of semi-automated workflows guided by dislocation theory.

Main Results:

• AI methods enable time-resolved studies of dislocation dynamics.
• Dimensional reduction of complex DFXM datasets is achieved.
• Successful application to studying thermally activated dislocation motion and networks.

Conclusions:

• AI significantly enhances the analysis of DFXM data for dislocation characterization.
• Physics-informed AI provides powerful tools for quantitative analysis of dislocation behavior.
• These integrated approaches unlock new insights from DFXM measurements in materials science.