ArticleNature communications2023
Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Beyond Imaging: Localized Synthesis, Fabrication, and Surface Modification via Scanning Electrochemical Cell Microscopy.ACS nano · 2026Article
- Self-Driving Scanning Probe Microscopy: From Acceleration to Discovery and Manipulation.Accounts of chemical research · 2026Article
- Self-evolving experimental platform for 3D sand printing.National science review · 2026Article
- Review
- Adaptive X-ray imaging with reinforcement learning.Journal of synchrotron radiation · 2026Article
- Machine-learning-assisted photonic device development: a multiscale approach from theory to characterization.Nanophotonics (Berlin, Germany) · 2025Review
- Evaluating large language model agents for automation of atomic force microscopy.Nature communications · 2025Article
- Deep learning of structural morphology imaged by scanning X-ray diffraction microscopy.Scientific reports · 2025Article
- Advancing Multicolor Super-Resolution Volume Imaging: Illuminating Complex Cellular Dynamics.JACS Au · 2025Review
- Compressive electron backscatter diffraction imaging.Journal of microscopy · 2025Article
- Minimal residual disease as a target for liquid biopsy in patients with solid tumours.Nature reviews. Clinical oncology · 2025Review
- Optimal Sparse Energy Sampling for X-ray Spectro-Microscopy: Reducing the X-ray Dose and Experiment Time Using Model Order Reduction.Chemical & biomedical imaging · 2024Article
- Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments.Journal of synchrotron radiation · 2023Article
- Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Modern scanning microscopes can image materials with up to sub-atomic spatial and sub-picosecond time resolutions, but these capabilities come with large volumes of data, which can be difficult to store and analyze. We report the Fast Autonomous Scanning Toolkit (FAST) that addresses this challenge by combining a neural network, route optimization, and efficient hardware controls to enable a self-driving experiment that actively identifies and measures a sparse but representative data subset in lieu of the full dataset. FAST requires no prior information about the sample, is computationally efficient, and uses generic hardware controls with minimal experiment-specific wrapping. We test FAST in simulations and a dark-field X-ray microscopy experiment of a WSe
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.