ArticleNature2021
Origins of structural and electronic transitions in disordered silicon.
Article in Nature, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 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
66 citing papers in PubMed.
- Direct correspondence between liquid and glass polymorphism in densely packed metallic alloys.Nature communications · 2026Article
- High-precision stress prediction using deep energy network enhanced by stress equilibrium with Delaunay integration on refined grid.Cell reports methods · 2026Article
- A universal machine learning model for the electronic density of states.Digital discovery · 2026Article
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- Multiscale modeling of graphene and carbon nanostructures: advances in atomistic, coarse-grained, and machine learning approaches.RSC advances · 2026Review
- Gaussian Basis Sets for All-Electron Excited-State Calculations of Large Molecules.Journal of chemical theory and computation · 2026Article
- Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation.Nano-micro letters · 2026Review
- Evidential deep learning for interatomic potentials.Nature communications · 2025Article
- Toward AI ecosystems for electrolyte and interface engineering in solid-state batteries.Science advances · 2025Review
- Weighted active space protocol for multireference machine-learned potentials.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- An automated framework for exploring and learning potential-energy surfaces.Nature communications · 2025Article
- Thermal Transport of Amorphous Hafnia Across the Glass Transition.ACS materials letters · 2025Article
- Using graph neural network and symbolic regression to model disordered systems.Scientific reports · 2025Article
- MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules.Journal of the American Chemical Society · 2025Article
- Signatures of paracrystallinity in amorphous silicon from machine-learning-driven molecular dynamics.Nature communications · 2025Article
- Machine Learning Advances in High-Entropy Alloys: A Mini-Review.Entropy (Basel, Switzerland) · 2024Review
- Slow Voltage Relaxation of Silicon Nanoparticles with a Chemo-Mechanical Core-Shell Model.ACS applied materials & interfaces · 2024Article
- Structure, Properties, and Applications of Silica Nanoparticles: Recent Theoretical Modeling Advances, Challenges, and Future Directions.Small (Weinheim an der Bergstrasse, Germany) · 2024Review
- Hyperparameter Optimization for Atomic Cluster Expansion Potentials.Journal of chemical theory and computation · 2024Article
- Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Structurally disordered materials pose fundamental questions
Identifiers
33408379What 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.