AI helps microscopes find the most informative nanoscale features in a sample
Researchers at the Department of Energy's Oak Ridge National Laboratory (ORNL) have developed an artificial intelligence framework that helps researchers use atomic force microscopes to identify important nanoscale features while autonomously targeting the most informative areas of a sample for closer study.
Researchers at Oak Ridge National Laboratory have created an artificial intelligence system called SimuScan to help atomic force microscopes more efficiently identify essential nanoscale features in samples. This tool takes the guesswork out of operating AFMs, which require expert judgment to determine where to scan, adjust settings, and analyze features.
Atomic force microscopy reveals structures as small as molecules, but using the instrument still depends on experienced personnel. SimuScan addresses this issue by generating realistic synthetic images with automatic labels, enabling AI models to be trained without extensive hand-annotated data. After training on synthetic data, the AI accurately identifies features in real AFM images, improving the speed, consistency, and scalability of high-throughput research.
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