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ORNL's SimuScan framework leverages synthetic data generation to automate target selection for atomic force microscopy, significantly reducing the manual labeling burden typically required by experts. By simulating realistic imaging imperfections like tip effects and scanner drift, the system enables faster, more consistent research workflows without relying on massive datasets of real-world images. This advancement directly addresses the cognitive load of operating high-end nanoscale instruments through autonomous decision-making rather than incidental software features.

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ORNL’s SimuScan cuts data labeling time for atomic force microscopes

Quantum ZeitgeistSeptember 10, 20265 min read

Researchers at ORNL developed SimuScan, an AI framework that cuts data labeling time for atomic force microscopes by autonomously targeting informative sample regions.

Oak Ridge National Laboratory researchers have developed SimuScan, an artificial intelligence framework that guides atomic force microscopes toward the most informative areas of a sample for closer study. Although atomic force microscopy reveals structures as small as molecules, operating the instrument traditionally requires expert judgment regarding scan location and settings; SimuScan aims to reduce this burden and enable faster, more consistent research. “Operating an atomic force microscope is a bit like piloting a modern jet,” said Liam Collins, an ORNL senior R&D scientist at the Center for Nanophase Materials Sciences (CNMS).…

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