Our laboratory combines automated microscopy with AI-assisted materials analysis to deliver fast, accurate, and quantitative insights from microstructures. We train powerful machine learning and deep learning models for defect detection, structure classification, property correlations, and microstructure quantification using frameworks such as TensorFlow, Keras, PyTorch, OpenCV, and scikit-learn. Feasibility studies are carried out using both existing datasets and newly acquired samples.

Our AI pipelines emphasize reproducibility and compliance through custom model exports and standardized AI system classification and validation. We ensure transparency through detailed model and data cards documenting architecture and limitations, while integrated data drift monitoring helps maintain accuracy over time and ensures reliability, robustness, and audit readiness.