ArticleNature communications2024
Modelling atomic and nanoscale structure in the silicon-oxygen system through active machine learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 64 citations in OpenAlex.
- The Applications of Machine Learning in Micro-Nano Materials Research: From High-Throughput Screening to Intelligent Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Ion correlations explain kinetic selectivity in diffusion-limited solid-state synthesis reactions.Nature materials · 2026Article
- aims-PAX: Parallel Active Exploration Enables Expedited Construction of Machine Learning Force Fields for Molecules and Materials.Journal of chemical information and modeling · 2026Article
- How Realistic Are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper-Water Interfaces.ACS materials Au · 2026Article
- Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation.Nano-micro letters · 2026Review
- Active learning potentials for first-principles phase diagrams using replica-exchange nested sampling.npj computational materials · 2026Article
- Silicon Photocatalytic Water-Treatment: Synthesis, Modifications, and Machine Learning Insights.Nanomaterials (Basel, Switzerland) · 2025Review
- Full-cycle device-scale simulations of memory materials with a tailored atomic-cluster-expansion potential.Nature communications · 2025Article
- An Atomistic Study of Reactivity in Solid-State Electrolyte Interphase Formation for Li/LiThe journal of physical chemistry. C, Nanomaterials and interfaces · 2025Article
- An automated framework for exploring and learning potential-energy surfaces.Nature communications · 2025Article
- Signatures of paracrystallinity in amorphous silicon from machine-learning-driven molecular dynamics.Nature communications · 2025Article
- Is the Future of Materials Amorphous? Challenges and Opportunities in Simulations of Amorphous Materials.ACS physical chemistry Au · 2025Review
- Prediction rigidities for data-driven chemistry.Faraday discussions · 2025Article
- Hyperparameter Optimization for Atomic Cluster Expansion Potentials.Journal of chemical theory and computation · 2024Article
- General-purpose machine-learned potential for 16 elemental metals and their alloys.Nature communications · 2024Article
- Spatially Resolved Uncertainties for Machine Learning Potentials.Journal of chemical information and modeling · 2024Article
- Modelling atomic and nanoscale structure in the silicon-oxygen system through active machine learning.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Silicon-oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis. Beyond the well-known silicon dioxide, there are phases with different stoichiometric composition and nanostructured composites. One of the key challenges in understanding the Si-O system is therefore to accurately account for its nanoscale heterogeneity beyond the length scale of individual atoms. Here we show that a unified computational description of the full Si-O system is indeed possible, based on atomistic machine learning coupled to an active-learning workflow. We showcase applications to very-high-pressure silica, to surfaces and aerogels, and to the structure of amorphous silicon monoxide. In a wider context, our work illustrates how structural complexity in functional materials beyond the atomic and few-nanometre length scales can be captured with active machine learning.
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.