DOE OSTI · 3030400
Synthetic data-driven deep learning for label-free autonomous atomic force microscopy
Abstract
Atomic force microscopy (AFM) is a widely used tool for nanoscale characterization across materials science, energy research, and biology. However, its adoption in high-throughput materials discovery and statistically driven studies remains limited by a strong dependence on expert operator input and by the scarcity of annotated experimental AFM datasets needed to enable data-driven automation. Here, we introduce SimuScan, a synthetic-data–driven framework that enables reliable AFM feature identification, segmentation, and targeted imaging without requiring large manually labeled experimental datasets. SimuScan generates tunable, high-fidelity synthetic AFM images of defined morphologies while incorporating realistic experimental artifacts, including tip–sample convolution, noise, flattening distortions, and surface debris. These datasets are shown to support scalable, label-free training of modern deep learning models for AFM analysis. When integrated into data-driven AFM workflows, SimuScan-trained models can locate and analyze nanoscale structures across large datasets and guide targeted follow-up imaging. We validate this approach on nanostructured surfaces, DNA assemblies, and bacterial cells, demonstrating robust generalization across diverse sample types with minimal operator intervention. More broadly, this work establishes a general strategy for generating explicitly conditioned, task-relevant synthetic data to improve the reliability of downstream models in autonomous microscopy.
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Millan-Solsona, Ruben [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)] (ORCID:0000000309127246), Checa Nualart, Marti [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)] (ORCID:0000000326076866), Brown, Spenser R. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000236658133), Bible, Amber N. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000274310533), Srijanto, Bernadeta [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)] (ORCID:0000000211881267), Wiggins, Laura [University of Sheffield (United Kingdom)], Madugula, Sita Sirisha [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)] (ORCID:000000019944117X), Pyne, Alice L. B. [University of Sheffield (United Kingdom)] (ORCID:0000000226588987), Morrell-Falvey, Jennifer L. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000293627528), Retterer, Scott [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000185341979), Vasudevan, Rama K. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)] (ORCID:0000000346928579), Collins, Liam [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)] (ORCID:0000000349469195). 2026-03-10. Synthetic data-driven deep learning for label-free autonomous atomic force microscopy. https://doi.org/10.1038/s41467-026-70421-3
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