AI Framework Guides Atomic Force Microscopes to Key Nanoscale Features

Researchers at the Department of Energy’s Oak Ridge National Laboratory (ORNL) have unveiled an artificial‑intelligence framework that enhances the use of atomic force microscopes (AFMs). The system automatically identifies nanoscale features of interest and directs the microscope to the most informative regions of a sample, allowing scientists to focus their measurements on areas that yield the highest scientific value.

The AI framework processes AFM data in real time, flagging structural anomalies, surface roughness variations, and other critical characteristics that may indicate material properties or defects. By autonomously selecting target zones, the tool reduces the time and manual effort required to scan large or complex specimens, improving throughput for research in nanotechnology, materials science, and surface engineering.

ORNL’s development represents a step toward more efficient nanoscale imaging and analysis. The framework is expected to accelerate discoveries in fields ranging from semiconductor fabrication to biomedical diagnostics by enabling researchers to obtain high‑quality data with greater precision and speed.