Artificial Intelligence in Materials Characterization: Methods, Applications, and Future Perspectives

Abstract

Materials characterization relies on techniques such as electron microscopy, diffraction, and spectroscopy to extract structural, chemical, and physical information from materials. While these traditional methods provide essential insights, their manual interpretation is time-consuming, operator-dependent, and difficult to scale for high-throughput analysis. In recent years, artificial intelligence (AI) has emerged as a transformative approach, enabling automation in image and pattern recognition, improving reproducibility, and accelerating property inference from experimental data. This paper reviews the current applications of AI in materials characterization, including microscopy, spectroscopy, and mechanical testing, and explains how algorithms assist in segmentation, phase identification, peak analysis, and predictive modelling. The review also discusses challenges such as data quality, model interpretability, and standardization, along with future opportunities like physics-informed learning, multimodal data fusion, and self-driving laboratories. Overall, AI is shown not only to enhance existing characterization workflows but also to redefine how materials data are generated, interpreted, and utilized in scientific decision-making.