ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022
Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 60 citations in OpenAlex.
- Straintronics and Twistronics in 2D Materials: Structural Control and Predictive Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Self-Driving Scanning Probe Microscopy: From Acceleration to Discovery and Manipulation.Accounts of chemical research · 2026Article
- Accelerating Structure-Property Relationship Discovery with Multimodal Machine Learning and Self-Driving Microscopy.ACS nano · 2026Article
- Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments.ACS nanoscience Au · 2026Article
- Real-time experiment-theory closed-loop interaction for autonomous materials science.Science advances · 2025Article
- A machine-learning-driven data labeling pipeline for scientific analysis inJournal of applied crystallography · 2025Article
- Science acceleration and accessibility with self-driving labs.Nature communications · 2025Review
- Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures.Advanced materials (Deerfield Beach, Fla.) · 2024Review
- Explainability and human intervention in autonomous scanning probe microscopy.Patterns (New York, N.Y.) · 2023Article
- Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Automatic parameter selection for electron ptychography via Bayesian optimization.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Physics-driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here, this work develops and experimentally implements a deep kernel learning (DKL) workflow combining the correlative prediction of the target functional response and its uncertainty from the structure, and physics-based selection of acquisition function, which autonomously guides the navigation of the image space. Compared to classical Bayesian optimization (BO) methods, this approach allows to capture the complex spatial features present in the images of realistic materials, and dynamically learn structure-property relationships. In combination with the flexible scalarizer function that allows to ascribe the degree of physical interest to predicted spectra, this enables physical discovery in automated experiment. Here, this approach is illustrated for nanoplasmonic studies of nanoparticles and experimentally implemented in a truly autonomous fashion for bulk- and edge plasmon discovery in MnPS
Indexed as
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.