Invited Review on Correlative Microscopy for AI-Driven Microstructure Characterization

Following my invited presentation at the IMAT Conference, I was honored to contribute an invited review article to Metallography, Microstructure, and Analysis.

The review addresses one of the key challenges in applying artificial intelligence to microstructure characterization: the availability of reliable ground-truth data. While AI has demonstrated considerable potential for automating microstructure analysis, its performance ultimately depends on the quality of the datasets used for training. For complex microstructures, generating these datasets remains difficult because manual annotation is both time-consuming and subject to expert interpretation.

The article explores how correlative microscopy can overcome these limitations by combining complementary characterization techniques, including optical microscopy, scanning electron microscopy (SEM), and electron backscatter diffraction (EBSD). Through several case studies, the review demonstrates how these methods can be integrated to create robust and reliable ground-truth datasets for AI applications.

A central concept of this work is to leverage correlative microscopy as a one-time investment for generating high-quality training data. Once an AI model has been trained on reliable ground truth, routine microstructure characterization can be performed using simpler and faster microscopy techniques, significantly improving efficiency without compromising reliability.

This review reflects our ongoing research interests in microstructure characterization, correlative microscopy, and the integration of artificial intelligence into materials science workflows. It also highlights the importance of combining advanced characterization methods with data-driven approaches to enable the next generation of automated materials analysis.

You can find the article here: https://link.springer.com/article/10.1007/s13632-026-01379-3.