"Computational and Machine Learning Approaches for Accelerated Materials Characterization: Methods, Challenges, and Future Perspectives"

Superalloys are essential to aerospace engineering since they possess better mechanical properties and high-temperature resistance.Nanomaterials are significant in the design of electronic and diagnostic medical devices.They provide better electrical, optical, and biological properties.The development of superalloys continues to advance technology and solve complex social issues.The design and optimization of superalloys with specific properties are essential to achieve better performance, durability, and functionality in other uses.Superalloys are important not just due to their physical properties but also due to their contribution toward solving global challenges like energy efficiency, environmental sustainability, and resource management.They spread their influence beyond physical properties to rend er them central to strategies of sustainable development.This makes characterization instrumental.Materials characterization typically relies heavily on established procedures.However, the passion towards technological advancement has introduced changes such that computational methods more and more supplement and enhance these traditional methods.Although computational materials characterization may involve multiple imaging modalities, this review primarily focuses on SEM image-based analytical frameworks owing to their suitability for automated microstructural interpretation [1].SEM imaging provides sufficient spatial resolution and morphological information necessary for statistical feature ABSTRACT: Characterization of advanced materials is essential for establishing reliable correlations between microstructural features and functional performance.Although imaging techniques such as Scanning Electron Microscopy (SEM) enable detailed surface-level observation, manual analysis of large-scale microstructural datasets often constrains interpretation efficiency and consistency.Emerging computational modelling and machine learning approaches offer automated alternatives for extracting quantitative information from SEM images to support predictive material evaluation.However, challenges related to the absence of unified computational workflows, limited availability of labelled datasets, and variations in imaging conditions continue to hinder the robustness of machine learning-assisted characterization systems.This review presents a focused examination of computational methodologies applied to SEM image analysis, including image preprocessing, feature representation, segmentation strategies, and supervised learning-based classification techniques.In addition, key limitations such as data imbalance, annotation uncertainty, domain variability, and model overfitting are critically discussed.The role of statistical validation procedures and performance assessment metrics in improving segmentation and classification reliability is also addressed.The findings underscore the importance of standardized benchmarking frameworks and multimodal data integration for enhancing the reliability and industrial applicability of intelligent materials characterization pipelines.