Atomic Force Microscopy

Atomic force microscopy (AFM) is a powerful technique for imaging surfaces at the nanoscale, with current research focusing on automating data analysis and enhancing 3D reconstruction capabilities. This involves the application of machine learning algorithms, including Bayesian optimization, deep learning models (like U-Nets and neural radiance fields), and recurrent neural networks, to improve image processing, feature extraction (such as molecular fingerprints), and the identification of multiple optima in complex datasets. These advancements are significantly improving the efficiency and accuracy of AFM, enabling broader applications in materials science, polymer science, and biological studies, including automated material characterization and 3D protein structure prediction.