This project aims to enhance nanoscale imaging by integrating advanced Atomic Force Microscopy (AFM) data with AI vision algorithms. It will transform raw AFM signals into physically meaningful maps of properties like stiffness and adhesion, enabling AI to automatically detect and classify nanoscale features.
The project will develop deep learning models trained on multiparametric AFM datasets to improve material and biological analysis. Calibration techniques will ensure accurate interpretations, minimizing noise and artifacts. The system will be tested on complex samples, including biomolecules and advanced materials.
This approach will improve nanoscale characterization across disciplines, enhancing imaging accuracy and AI interpretability. Future updates will incorporate new data and methodologies to refine analysis further.