This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: 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.
"The challenge is not only acquiring the image but also understanding what is in the image, deciding what matters and knowing where the microscope should look next," said Ruben Millan Solsona, an ORNL technical professional and staff scientist. In a paper published in Nature Communications, the researchers describe how SimuScan addresses one of AI's biggest obstacles for AFM: the shortage of high-quality labeled training data. At first glance, interpreting AFM images looks like a standard image-analysis problem.
But AFM images differ fundamentally from photographs because they reflect both the sample and the measurement process. "A camera records reflected light, but an atomic force microscope acts more like a high-tech record player needle feeling its way across a landscape," Collins said. What the microscope records depends on the sample, the AFM probe itself and the way it is measured.
Millan Solsona put it simply: "Tip geometry, drift, flattening and contamination can all introduce artifacts that resemble real nanoscale structures. Experienced users learn to distinguish them; AI models must be taught to do the same." Training data are also scarce. Unlike everyday photographs or medical images, relatively few AFM images have been carefully labeled by experts, making it difficult to train AI models.
SimuScan tackles the data problem by generating synthetic AFM images, along with automatic labels tied directly to the simulated object geometry, so models can be trained without large volumes of hand-annotated experimental data. To work in real laboratories, however, the synthetic images must be realistic. Rather than producing pristine images, SimuScan recreates the imperfections AFM users encounter every day—including tip effects, scanner drift, electronic noise, contamination and surface roughness.
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