AI helps microscopes find the most informative nanoscale features in a sample
This article has been reviewed according to Science X's editorial process and policies .
This article has been reviewed according to Science X's editorial process and policies .
The short version
- 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).
What happened
"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.
Why it matters
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.
Summary by Nerd News Network. Read the full article at Phys.org via the links above and below.