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.

Although atomic force microscopy (AFM) reveals structures as small as molecules, operating the instrument still requires expert judgment about where to scan, how to adjust settings and which features deserve closer study. SimuScan reduces that burden, making AFM faster, more consistent and better suited for high-throughput 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). “The hardware has incredible capability, but making full use of it often requires an experienced pilot.” That reliance on specialized expertise slows large-scale studies and makes results more dependent on individual users.