ArticleACS nano2024
Automated Structure Discovery for Scanning Tunneling Microscopy.
Article in ACS nano, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Automating Model Building for SPM Images of Biomolecules Using MISO.Journal of chemical information and modeling · 2026Article
- Self-Driving Scanning Probe Microscopy: From Acceleration to Discovery and Manipulation.Accounts of chemical research · 2026Article
- Structure Prediction of Organic/Inorganic Interfaces with Genarris.Journal of chemical theory and computation · 2026Article
- UniMR: A Plug-and-Play Framework of Automated Molecular Recognition for Scanning Tunneling Microscopy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Scanning tunneling microscopy (STM) with a functionalized tip apex reveals the geometric and electronic structures of a sample within the same experiment. However, the complex nature of the signal makes images difficult to interpret and has so far limited most research to planar samples with a known chemical composition. Here, we present automated structure discovery for STM (ASD-STM), a machine learning tool for predicting the atomic structure directly from an STM image, by building upon successful methods for structure discovery in noncontact atomic force microscopy (nc-AFM). We apply the method on various organic molecules and achieve good accuracy on structure predictions and chemical identification on a qualitative level while highlighting future development requirements for ASD-STM. This method is directly applicable to experimental STM images of organic molecules, making structure discovery available for a wider scanning probe microscopy audience outside of nc-AFM. This work also allows more advanced machine learning methods to be developed for STM structure discovery.
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Registered trials
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