Metadata-Driven Optimization in Electron Microscopy: improving SEM imaging data through FAIR practices

The scanning electron microscope (SEM) is an essential tool for characterizing the micro- and nanostructural properties of solid samples due to its high resolution and versatility. SEM plays a critical role in various fields, including materials science and biology, where high-quality imaging is necessary. Despite its widespread use, optimizing SEM instrumental parameters for enhanced imaging remains a challenge due to the complexity and interdependence of these parameters. Traditional manual adjustments are time-consuming, require expert knowledge, and introduce variability, underscoring the need for automated optimization methods. This research explores the integration of machine learning techniques to optimize SEM imaging parameters, focusing on accelerating voltage, working distance, and aperture size. By implementing an AI-driven recommendation system leveraging instrument- generated metadata, we aim to enhance image quality while improving efficiency and reproducibility. A full-factorial experimental design was implemented to systematically investigate the interactions among imaging parameters. Image quality was evaluated using two no-reference metrics, NIQE (Natural Image Quality Evaluator) and BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator), in conjunction with extracted metadata and expert visual assessments. A polynomial regression model was trained on both NIQE and BRISQUE scores to predict optimal imaging conditions. Although NIQE out performed BRISQUE, neither metric fully captures the specific characteristics of SEM imaging. This study therefore also initiates an effort to develop more accurate, SEM-specific quality metrics, with the aim of enabling robust, fully automated optimization pipelines in the near future. The methodology adheres to FAIR data principles, ensuring that data is Findable, Accessible, Interoperable, and Reusable. The research also addresses metadata standards and lifecycle management for SEM imaging datasets, facilitating transparency and reproducibility in scientific workflows. The proposed AI-driven approach significantly reduces operator intervention, enhances the consistency of SEM imaging, and accelerates the imaging process. Comparative analyses demonstrate that the recommendation system outperforms traditional manual tuning, offering a scalable solution adaptable to a wide range of sample types and experimental settings. By leveraging machine learning for SEM parameter optimization, this study contributes to the advancement of automated, high-quality imaging in electron microscopy, reducing human dependency while ensuring accurate and reproducible results. The findings highlight the potential of AI-driven automation in SEM imaging, paving the way for future research in intelligent microscopy systems and broader applications in materials characterization and biomedical imaging.