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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Data availability
The datasets and trained models generated in this study have been deposited in the Zenodo repository (https://zenodo.org/records/17037230 and https://zenodo.org/records/−17179726). All source data supporting the figures are provided with this paper.
Code availability
The analysis and training scripts are available on GitHub at https://github.com/Rmillansol/SimuScan-AFfMtools.git. A citable archived version of the code has been deposited in Zenodo (https://doi.org/10.5281/zenodo.18665434)53. The SimuScan synthetic data generator is distributed as a standalone command-line executable and is publicly available via Zenodo (https://zenodo.org/records/18134911), together with example configuration files and documentation.
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Acknowledgements
This work was supported by the U.S. Department of Energy, Office of Science FWP ERKCZ64, Structure Guided Design of Materials to Optimize the Abiotic-Biotic Material Interface, as part of the the Biopreparedness Research Virtual Environment (BRaVE) initiative. AFM measurements, sample preparation, and image analysis were conducted as part of a user project at the Center for Nanophase Materials Sciences (CNMS), which is a US Department of Energy, Office of Science User Facility at Oak Ridge National Laboratory. We also acknowledge support from a UKRI Future Leaders Fellowship (MR/W00738X/1) and the Henry Royce Institute for Advanced Materials, funded through EPSRC grants EP/R00661X/1, EP/S019367/1, EP/P02470X/1 and EP/P025285/1 (A.L.B.P.). U.S. Department of Energy, Office of Science FWP ERKCZ64, Structure Guided Design of Materials to Optimize the Abiotic-Biotic Material Interface.
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R.M.-S. and L.C. conceived the study (Conceptualization) and developed the methodology. R.M.-S. led the software development, while validation and formal analysis was assisted by M.C., S.S.M., R.K.V., and L.C. Experimental investigation was performed by R.M.-S. Resources were provided by S.R.B., B.S., A.N.B., J.L.M.-F., A.P., and L.W. Data curation was handled by R.M.-S., M.C., and L.C. Data visualization was conducted by R.M.-S., M.C., and L.C. L.C. supervised and administered the project. The original draft was written by L.C. and R.M.-S., and all authors reviewed and edited the manuscript. Funding was acquired by L.C. and S.R.
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Millan-Solsona, R., Checa, M., Brown, S.R. et al. Synthetic data-driven deep learning for label-free autonomous atomic force microscopy. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70421-3
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DOI: https://doi.org/10.1038/s41467-026-70421-3
